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ChatGPT: The Agentic App
30 september 2025 · 9 min
Ever since ChatGPT exploded in popularity, there has been a looming “how” to its monetization plans. Much has been said about shopping and advertising as the likely paths, especially with Fidji Simo joining as CEO of Applications under Sam Altman. Lees meer
Advertising as a business model for AI is logical but difficult to personalize and specialize. We know tons of people spend a lot of time using AI models, but how do you best get the sponsored content into the outputs? This is an open technical problem, with early efforts from the likes of Perplexity falling short.
Shopping is another, but the questions have long been whether AI models actually have the precision to find the items you want, to learn exactly what you love, and to navigate the web to handle all the corner cases of checkouts. These reflect a need for increased capabilities on known AI benchmarks, rather than inventing a new way of serving ads. OpenAI’s o3 model was a major step up in search functionality, showing it was viable; the integration was either a business problem — where OpenAI had to make deals — or an AI one — where ChatGPT wasn’t good enough at managing websites for you.
Yesterday, ChatGPT launched its first integrated shopping push with Buy It in ChatGPT, a simple checkout experience, and an integrated commerce backend built on the Agentic Commerce Protocol (ACP). The announcement comes with the perfect partners to complement the strengths of OpenAI’s current models. GPT-5-Thinking is the best at finding niche content on the web, and ChatGPT’s launch partner for shopping is Shopify (*soon, Etsy is available today), the home to the long tail of e-commerce merchants of niche specialties. If this works, it will let users actively uncover exactly what they are looking for — from places that were often hard to impossible to find on Google.
This synergy is a theme we’ll see reoccur in other agents of the future. The perfect model doesn’t make a useful application unless it has the information or …
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Thinking, Searching, and Acting
22 september 2025 · 9 min
The weaknesses of today’s best models are far from those of the original ChatGPT — we see they lack speed, we fear superhuman persuasion, and we aspire for our models to be more autonomous. These models are all reasoning models that have long surpassed the original weaknesses of ChatGPT-era language models, hallucinations, total lack of recent information, complete capitulations, and other hiccups that looked like minor forms of delusion laid on top of an obviously spectacular new technology. Lees meer
Reasoning models today are far more complex than the original chatbots that consisted of standalone model weights (and other lightweight scaffolding such as safety filters). They're built on three primitives that'll be around for years to come:
* Thinking: The reasoning traces that enabled inference-time scaling. The "thoughts" of a reasoning model take a very different form than those of humans that inspired the terminology used like Chain of Thought (CoT) or Thinking models.
* Searching: The ability to request more, specific information from non-parametric knowledge stores designed specifically for the model. This fills the void set by how model weights are static but living in a dynamic world.
* Acting: The ability for models to manipulate the physical or digital world. Everything from code-execution now to real robotics in the future allow language models to contact reality and overcome their nondeterministic core. Most of these executable environments are going to build on top of infrastructure for coding agents.
These reasoning language models, as a form of technology are going to last far longer than the static model weights that predated and birthed ChatGPT. Sitting just over a year out from the release of OpenAI's o1-preview on September 12, 2024, the magnitude of this is important to write in ink. Early reasoning models with astounding evaluation scores were greeted with resounding criticism of “they won’t generalize,” but that has turned out to be resoundingly …
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Coding as the epicenter of AI progress and the path to general agents
18 september 2025 · 16 min
Coding, due to its breadth of use-cases, is arguably the last tractable, general domain of continued progress for frontier models that most people can interface with. This is a bold claim, so let’s consider some of the other crucial capabilities covered in the discourse of frontier models: Lees meer
* Chat and the quality of prose written by models has leveled off, other than finetuning to user measures such as sycophancy.
* Mathematics has incredible results, but very few people directly gain from better theoretical mathematics.
* The AIs’ abilities to do novel science are too unproven to be arguable as a target of hillclimbing.
Still, coding is a domain where the models are already incredibly useful, and they continue to consistently stack on meaningful improvements. Working daily with AI over the last few years across side projects and as an AI researcher, it has been easy to take these coding abilities for granted because some forms of them have been around for so long. We punt a bug into ChatGPT and it can solve it or autocomplete can tab our way through entire boilerplate.
These use-cases sound benign, and haven’t changed much in that description as they have climbed dramatically in capabilities. Punting a niche problem in 1000+ lines of code to GPT-5-Pro or Gemini Deep Think feels like a very fair strategy. They really can sometimes solve problems that a teammate or I were stuck on for hours to days. We’re progressing through this summarized list of capabilities:
* Function completion: ~2021, original Github CoPilot (Codex)
* Scripting: ~2022, ChatGPT
* Building small projects: ~2025, CLI agents
* Building complex production codebases, ~2027 (estimate, which will vary by the codebase)
Coding is maybe the only domain of AI use where I’ve felt this slow, gradual improvement. Chat quality has been “good enough” since GPT-4, search showed up and has been remarkable since OpenAI’s o3. Through all of these more exciting moments, AIs’ coding abilities have just con…
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On China's open source AI trajectory
9 september 2025 · 14 min
Hello everyone! I’m coming back online after two weeks of vacation. Thankfully it coincided with some of the slowest weeks of the year in the AI space. I’m excited to get back to writing and (soon) share projects that’ll wrap up in the last months of the year. Lees meer
It seemed like a good time to remind people of the full set of housekeeping for Interconnects.
* Many people love the audio version of the essays (read by me, not AI). You can get them in your podcast player here. Paid subscribers can add private podcast feeds under “manage your subscription” where voiceover is available for paywalled posts.
* The Interconnects Discord for paid subscribers continues to get better, and is potentially the leading paid perk amid the fragmentation of Twitter etc.
* We’re going to be rolling out more perks for group subscriptions and experimental products this fall. Stay tuned, or get in touch if group discounts are super exciting for your company.
For the time being, I’m planning trips and meetups across a few conferences in October. I’ll be speaking at The Curve (Oct. 3-5, Berkeley), COLM (Oct. 7-10, Montreal, interest form), and the PyTorch Conference (Oct. 21-24, SF) on open models, Olmo, and the ATOM Project, so stay tuned for meetups and community opportunities. On to the post!
China is maneuvering to double down on its open AI ecosystem. Depending on how the U.S. and its allies change culture and mobilize investment, this could make the dominance of Chinese AI models this summer, from Qwen, Kimi, Z.ai, and DeepSeek, looks like foreshadowing rather than the maximum gap in open models between the U.S. and China.
Until the DeepSeek moment, AI was likely a fringe issue to the PRC Government. The central government will set guidelines, rules, budgets, and focus areas that will be distributed and enforced across the decentralized government power structures. AI wasn’t a political focus and the strategy of open-source was likely set by companies looking to close the gap wit…
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Ranking the Chinese Open Model Builders
17 augustus 2025 · 13 min
The Chinese AI ecosystem has taken the AI world by storm this summer with an unrelenting pace of stellar open model releases. The flagship releases that got the most Western media coverage are the likes of Qwen 3, Kimi K2, or Zhipu GLM 4.5, but there is a long-tail of providers close behind in both quality and cadence of releases. Lees meer
In this post we rank the top 19 Chinese labs by the quality and quantity of contributions to the open AI ecosystem — this is not a list of raw ability, but outputs — all the way from the top of DeepSeek to the emerging open research labs. For a more detailed coverage of all the specific models, we recommend studying our Artifacts Log series, which chronicles all of the major open model releases every month. We plan to revisit this ranking and make note of major new players, so make sure to subscribe.
At the frontier
These companies rival Western counterparts with the quality and frequency of their models.
DeepSeek
deepseek.com | 🤗 deepseek-ai | X @DeepSeek_AI
DeepSeek needs little introduction. Their V3 and R1 models, and their impact, are still likely the biggest AI stories of 2025 — open, Chinese models at the frontier of performance with permissive licenses and the exposed model chains of thought that enamored users around the world.
With all the attention following the breakthrough releases, a bit more has been said about DeepSeek in terms of operations, ideology, and business model relative to the other labs. They are very innovative technically and have not devoted extensive resources to their consumer chatbot or API hosting (as judged by higher than industry-standard performance degradation).
Over the last 18 months, DeepSeek was known for making “about one major release a month.” Since the updated releases of V3-0324 and R1-0528, many close observers have been surprised by their lack of contributions. This has let other players in the ecosystem close the gap, but in terms of impact and actual commercial usage, DeepSeek is …
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Contra Dwarkesh on Continual Learning
15 augustus 2025 · 10 min
Dwarkesh Patel’s now well-read post on why he is extending his AI timelines focuses on the idea of continual learning. If you ask me, what we have already is AGI, so the core question is: Is continual learning a bottleneck on AI progress? Lees meer
In this post, I argue that continual learning as he describes it actually doesn’t matter for the trajectory of AI progress that we are on. Continual learning will eventually be solved, but in the sort of way that a new type of AI will emerge from it, rather than continuing to refine what it means to host ever more powerful LLM-based systems.
Continual learning is the ultimate algorithmic nerd snipe for AI researchers, when in reality all we need to do is keep scaling systems and we’ll get something indistinguishable from how humans do it, for free.
To start, here’s the core of the Dwarkesh piece as a refresher for what he means by continual learning.
Sometimes people say that even if all AI progress totally stopped, the systems of today would still be far more economically transformative than the internet. I disagree. I think the LLMs of today are magical. But the reason that the Fortune 500 aren’t using them to transform their workflows isn’t because the management is too stodgy. Rather, I think it’s genuinely hard to get normal humanlike labor out of LLMs. And this has to do with some fundamental capabilities these models lack.
I like to think I’m “AI forward” here at the Dwarkesh Podcast. I’ve probably spent over a hundred hours trying to build little LLM tools for my post production setup. And the experience of trying to get them to be useful has extended my timelines. I’ll try to get the LLMs to rewrite autogenerated transcripts for readability the way a human would. Or I’ll try to get them to identify clips from the transcript to tweet out. Sometimes I’ll try to get them to co-write an essay with me, passage by passage. These are simple, self contained, short horizon, language in-language out tasks - the kinds of assign…
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GPT-5 and the arc of progress
8 augustus 2025 · 11 min
If you want a video version of this, check out the last 20 minutes of the livestream reaction (edit, fixed link) I did with Will Brown of Prime Intellect and Swyx of Smol AI & Latent Space. Lees meer
GPT-5 was set up to fail on some of the narratives it was expected to satisfy. The two central themes it had to decide between were the AGI (or superintelligence) narrative that Sam Altman & co. have been using to fundraise and the fact that ChatGPT is one of the fastest-growing consumer technologies of all time.
To fulfill both, GPT-5 needed to be AGI while also being cheap enough to serve as the most-used AI system in the world. Business and technological realities made it inevitable that GPT-5’s primary impact would be to solidify OpenAI’s market position, even if it raises a lot of eyebrows for the long-term trajectory of AI.
The reactions online capture this as well. The OpenAI live streams have historically catered to AI insiders, but the product speaks entirely to a different audience. The people discussing this release on Twitter will be disappointed in a first reaction, but 99% of people using ChatGPT are going to be so happy about the upgrade. Confusingly enough, this includes many of the critics. GPT-5 is a good AI system. It’s right in line with best-in-class across pretty much every evaluation, while being cheap enough to serve the whole world.
OpenAI is largely fixing its product offering with an announcement that was hyped to be one of the biggest AI news cycles of the year. AI news being loud is defined by narratives being different more-so than technology being better. OpenAI releasing an open model again will likely be pinpointed as just as important a day for the arc of AI as the GPT-5 release. In many ways GPT-5 was set up to fail and that is very off-putting for those expecting maximum AI progress in the near term.
I’m not going to dwell on it, but oh boy, that was a messy release. GPT-5 being announced and rolled out like this is very odd. Countless pl…
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gpt-oss: OpenAI validates the open ecosystem (finally)
5 augustus 2025 · 14 min
OpenAI released two open-weight, text-only reasoning models today, both mixture of experts (MoE) sized to run efficiently on a range of hardware from consumer GPUs to the cloud. These models have the Apache 2.0 license, so they’re available for distillation into other reasoning models, deployment into commercial products, and are free of downstream restrictions. These two models, the smaller gpt-oss-20B with 3.6B active parameters and 21B total and the larger gpt-oss-120B with 5.1B active parameters, follow the trends we’ve seen with the other leading open models in architecture choices. Lees meer
Where this release shines is in the dramatic change in open model performance and strategy that comes with the leading name in AI releasing an open model that undercuts some of their own API products.
We’ll get to the technical details on the model later, but the main point of this post is how much OpenAI has changed by releasing their first open language model since GPT-2. The larger 120B model “achieves near-parity with OpenAI o4 mini on core reasoning benchmarks” and is a major moment for the ecosystem:
* OpenAI has released an open model at the frontier of current open model performance — highlighting how major concerns over open models that OpenAI leadership mentioned in 2023 were overblown. The marginal risks of open models have been shown to not be as extreme as many people thought (at least for text only — multimodal is far riskier). Once other organizations, particularly Meta and China showed OpenAI that there was no risk here, the path was opened to release a model.
* OpenAI has revealed far more of their technical stack than any release to date. This blog post has light details on many things in the model, but community tinkering will begin to better understand what is going on here. This includes basic things like our first time seeing a raw chain of thought (CoT) for an OpenAI reasoning model, but also more interesting things like how this model is trained to use …
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Towards American Truly Open Models: The ATOM Project
4 augustus 2025 · 22 min
I’m very excited to share a substantial project on invigorating investment in open language models and AI research in the U.S. The ATOM (American Truly Open Models) Project is the mature evolution of my original “American DeepSeek Project” and I hope it can help be a turning point in the current trajectory of losing open model relevance vis-a-vis China, and even the rest of the world. Lees meer
I’ve included the full text below, but I encourage you to visit the website for the full version with added visuals, data, and a place to sign your support. This is a community movement, rather than me fundraising, starting an organization, or anything like that
If you can help get the word out and or sign your support, I’d greatly appreciate it.
(Or watch a 5 minute overview on YouTube)
The ATOM Project: Towards fully open models for US research & industry
Reinvigorating AI research in the U.S. by building leading, open models at home
America's AI leadership was built by being the global hub and leading producer of open AI research, research which led directly to innovations like the Transformer architecture, ChatGPT, and the latest innovations in reasoning models and agents. America is poised to lose this leadership to China, in a period of geopolitical uncertainty and rising tensions between these two nations. America's best AI models have become more closed and restricted, while Chinese models have become more open, capturing substantial market share from businesses and researchers in the U.S. and abroad.
Open language models are becoming the foundation of AI research and the most important tool in securing this leadership. America has lost its lead in open models – both in performance and adoption – and is on pace to fall further behind. The United States must lead AI research globally, and we must invest in making the tools our researchers need to do their job here in America: a suite of leading, open foundation models that can re-establish the strength of the research ec…
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Interviewing Ross Taylor on the state of AI: Chinese open models, scaling reasoning, useful tools, and what comes next
29 juli 2025 · 1 uur 15 min
I’m excited to welcome Ross Taylor back on the podcast (and sorry for the lack of episodes in general – I have a lot going on!). The first time Ross came on we focused on reasoning – before inference-time scaling and that sort of RL was popular, agents, Galactica, and more from his Llama days. Since then, and especially after DeepSeek R1, Ross and I have talked asynchronously about the happenings of AI, so it’s exciting to do it face to face. Lees meer
In this episode we cover some of everything:
* Recent AI news (Chinese models and OpenAI’s coming releases)
* “Do and don’t” of LLM training organizations
* Reasoning research and academic blind spots
* Research people aren’t paying enough attention to
* Non language modeling news & other topics
Listen on Apple Podcasts, Spotify, YouTube, and where ever you get your podcasts. For other Interconnects interviews, go here.
Show outline as a mix of questions and edited assertions that Ross sent me as potential topics.
00:00 Recent AI news
Related reading is on Kimi’s K2 model, thoughts on OpenAI’s forthcoming open release.
* What did you think of Z.ai’s GLM 4.5 model (including MIT licensed base model) with very strong scores? And Kimi?
* What will OpenAI’s open model actually be?
* What do you make of the state of the ecosystem?
12:10 “Do and don’t” of LLM training organizations
Related reading is on managing training organizations or the Llama 4 release.
This is one of my favorite topics – I think a lot of great stuff will be written on it in the future. For now, Ross asserts…
* Most major LLM efforts are not talent-bound, but politics-bound. Recent failures like Llama 4 are org failures not talent failures.
* Most labs are chaotic, changing direction every week. Very different picture from the narrative presented online.
* Most labs represent investment banks or accountancy firms in that they hire smart young people as “soldiers” and deliberately burn them out with extremely long hours.
36:40 Reasoning resea…
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The White House's plan for open models & AI research in the U.S.
23 juli 2025 · 13 min
Today, the White House released its AI Action Plan, the document we’ve been waiting for to understand how the new administration plans to achieve “global dominance in artificial intelligence (AI).” There’s a lot to unpack in this document, which you’ll be hearing a lot about from the entire AI ecosystem. This post covers one narrow piece of the puzzle — its limited comments on open models and AI research investment. Lees meer
For some context, I was a co-author on the Ai2 official comment to the Office of Science and Technology Policy (OSTP) for the AI Action Plan and have had some private discussions with White House staff on the state of the AI ecosystem.
A focus of mine through this document is how the government can enable better fully open models to exist, rather than just more AI research in general, as we’re in a shrinking time window where if we don’t create better fully open models then the academic community could be left with a bunch of compute to do research on models that are not reflective of the frontier of performance and behavior. This is why I give myself ~18 months to finish The American DeepSeek Project.
Important context for this document is to consider what the federal government can actually do to make changes here. The executive branch has limited levers it can pull to disperse funding and make rules, but it sends important signaling to the rest of the government and private sector.
Overall, the White House AI Action Plan comes across very clearly that we should increase investment in open models, and for the right reasons.
This reflects a shift from previous federal policy, where the Biden executive order had little to say about open models other than them getting grouped into models needing pre-release testing if they were trained with more than 10^26 FLOPS (which led to substantial discussion on the general uselessness of compute thresholds as a policy intervention). Later, the National Telecommunications and Information Administration (NTIA) …
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Kimi K2 and when "DeepSeek Moments" become normal
14 juli 2025 · 7 min
https://www.interconnects.ai/p/kimi-k2-and-when-deepseek-moments Lees meer
The DeepSeek R1 release earlier this year was more of a prequel than a one-off fluke in the trajectory of AI. Last week, a Chinese startup named Moonshot AI dropped Kimi K2, an open model that is permissively licensed and competitive with leading frontier models in the U.S. If you're interested in the geopolitics of AI and the rapid dissemination of the technology, this is going to represent another "DeepSeek moment" where much of the Western world — even those who consider themselves up-to-date with happenings of AI — need to change their expectations for the coming years.
In summary, Kimi K2 shows us that:
* HighFlyer, the organization that built DeepSeek, is far from a uniquely capable AI laboratory in China,
* China is continuing to approach (or reached) the absolute frontier of modeling performance, and
* The West is falling even further behind on open models.
Kimi K2, described as an "Open-Source Agentic Model" is a sparse mixture of experts (MoE) model with 1T total parameters (~1.5x DeepSeek V3/R1's 671B) and 32B active parameters (similar to DeepSeek V3/R1's 37B). It is a "non-thinking" model with leading performance numbers in coding and related agentic tasks (earning it many comparisons to Claude 3.5 Sonnet), which means it doesn't generate a long reasoning chain before answering, but it was still trained extensively with reinforcement learning. It clearly outperforms DeepSeek V3 on a variety of benchmarks, including SWE-Bench, LiveCodeBench, AIME, or GPQA, and comes with a base model released as well. It is the new best-available open model by a clear margin.
These facts with the points above all have useful parallels for what comes next:
* Controlling who can train cutting edge models is extremely difficult. More organizations will join this list of OpenAI, Anthropic, Google, Meta, xAI, Qwen, DeepSeek, Moonshot AI, etc. Where there is a concentration of talent and sufficient compu…
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The American DeepSeek Project
4 juli 2025 · 11 min
https://www.interconnects.ai/p/the-american-deepseek-project Lees meer
While America has the best AI models in Gemini, Claude, o3, etc. and the best infrastructure with Nvidia it’s rapidly losing its influence over the future directions of AI that unfold in the open-source and academic communities. Chinese organizations are releasing the most notable open models and datasets across all modalities, from text to robotics or video, and at the same time it’s common for researchers worldwide to read far more new research papers from Chinese organizations rather than their Western counterparts.
This balance of power has been shifting rapidly in the last 12 months and reflects shifting, structural advantages that Chinese companies have with open-source AI — China has more AI researchers, data, and an open-source default.
On the other hand, America’s open technological champions for AI, like Meta, are “reconsidering their open approach” after yet another expensive re-org and the political environment is dramatically reducing the interest of the world’s best scientists in coming to our country.
It’s famous lore of the AI industry that much of the flourishing of progress around ChatGPT is downstream from Google Research’s, and the industry’s writ-large, practice of openly sharing the science of AI until approximately 2022. Stopping this practice, and the resulting power shifts mean it will be likely that the next “Transformer”-style breakthrough will be built on or related to Chinese AI models, AI chips, ideas, or companies. Countless Chinese individuals are some of the best people I’ve worked with, both at a technical and personal level, but this direction for the ecosystem points to AI models being less accountable, auditable, and trustworthy due to inevitable ties to the Chinese Government.
The goal for my next few years of work is what I’m calling The American DeepSeek Project — a fully open-source model at the scale and performance of current (publicly available) frontier mo…
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Some ideas for what comes next (Jun. 2025)
23 juni 2025 · 10 min
https://www.interconnects.ai/p/summertime-outlook-o3s-novelty-coming Lees meer
Summer is always a slow time for the tech industry. OpenAI seems fully in line with this, with their open model “[taking] a little more time” and GPT-5 seemingly always delayed a bit more. These will obviously be major news items, but I’m not sure we see them until August.
I’m going to take this brief reprieve in the bombardment of AI releases to reflect on where we’ve been and where we’re going. Here’s what you should know.
1. o3 as a technical breakthrough beyond scaling
The default story around OpenAI’s o3 model is that they “scaled compute for reinforcement learning training,” which caused some weird, entirely new over-optimization issues. This is true, and the plot from the livestream of the release still represents a certain type of breakthrough — namely scaling up data and training infrastructure for reinforcement learning with verifiable rewards (RLVR).
The part of o3 that isn’t talked about enough is how different its search feels. For a normal query, o3 can look at 10s of websites. The best description I’ve heard of its relentlessness en route to finding a niche piece of information is akin to a “trained hunting dog on the scent.” o3 just feels like a model that can find information in a totally different way than anything out there.
The kicker with this is that we’re multiple months out from its release in April of 2025 and no other leading lab has a model remotely like it. In a world where releases between labs, especially OpenAI and Google, seem totally mirrored, this relentless search capability in o3 still stands out to me.
The core question is when will another laboratory release a model that feels qualitatively similar? If this trend goes on through the end of the summer it’ll be a confirmation that OpenAI had some technical breakthrough to increase the reliability of search and other tool-use within reasoning models.
For a contrast, consider basic questions we are facing …
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Crafting a good (reasoning) model
18 juni 2025 · 30 min
Why are some models that are totally exceptional on every benchmark a total flop in normal use? This is a question I was hinting at in my post on GPT-4o’s sycophancy, where I described it as “The Art of The Model”: Lees meer
RLHF is where the art of the model is crafted and requires a qualitative eye, deep intuition, and bold stances to achieve the best outcomes.
In many ways, it takes restraint to land a great model. It takes saying no to researchers who want to include their complex methods that may degrade the overall experience (even if the evaluation scores are better). It takes saying yes to someone advocating for something that is harder to measure.
In many ways, it seems that frontier labs ride a fine line between rapid progress and usability. Quoting the same article:
While pushing so hard to reach the frontier of models, it appears that the best models are also the ones that are closest to going too far.
Once labs are in sight of a true breakthrough model, new types of failure modes and oddities come into play. This phase won’t last forever, but seeing into it is a great opportunity to understanding how the sausage is made and what trade-offs labs are making explicitly or implicitly when they release a model (or in their org chart).
This talk expands on the idea and goes into some of the central grey areas and difficulties in getting a good model out the door. Overall, this serves as a great recap to a lot of my writing on Interconnects in 2025, so I wanted to share it along with a reading list for where people can find more.
The talk took place at an AI Agents Summit local to me in Seattle. It was hosted by the folks at OpenPipe who I’ve been crossing paths with many times in recent months — they’re trying to take similar RL tools I’m using for research and make them into agents and products (surely, they’re also one of many companies).
Slides for the talk are available here and you can watch on YouTube (or listen wherever you get your podcasts).
Reading …
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The rise of reasoning machines
12 juni 2025 · 9 min
https://www.interconnects.ai/p/the-rise-of-reasoning-machines Lees meer
Note: voiceover coming later in the day. I may fix a couple typos then too.
A sufficiently general definition of reasoning I’ve been using is:
Reasoning is the process of drawing conclusions by generating inferences from observations.
Ross Taylor gave this definition on his Interconnects Interview, which I re-used on my State of Reasoning recap to start the year (and he’s expanded upon on his YouTube channel). Reasoning is a general space of behaviors or skills, of which there can be many different ways of expressing it. At the same time, reasoning for humans is very naturally tied to our experiences such as consciousness or free will.
In the case of human brains, we collectively know very little of how they actually work. We, of course, know extremely well the subjective experience of our reasoning. We do not know the mechanistic processes much at all.
When it comes to language models, we’re coming at it from a somewhat different angle. We know the processes we took to build these systems, but we also don’t really know “how deep learning works” mechanistically. The missing piece is that we don’t have a deep sense of the subjective experience of an AI model like we do with ourselves. Overall, the picture is quite similar.
To set the stage why this post is needed now, even when reasoning model progress has been rampaging across the technology industry in 2025. Last week, an Apple paper titled The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity reignited the “reasoning debate” with newfound vigor.
Some of the key examples in the paper, other than traditional reasoning evaluations such as MATH-500, were that AIs struggled to solve scaled up versions of toy problems, shown below. These are problems that one can programmatically increase the complexity on.
The argument was that language models cannot generalize to higher complex…
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What comes next with reinforcement learning
9 juni 2025 · 14 min
https://www.interconnects.ai/p/what-comes-next-with-reinforcement Lees meer
First, some housekeeping. The blog’s paid discord (access or upgrade here) has been very active and high-quality recently, especially parsing recent AI training tactics like RLVR for agents/planning. If that sounds interesting to you, it’s really the best reason to upgrade to paid (or join if you’ve been paying and have not come hung out in the discord).
Second, I gave a talk expanding on the content from the main technical post last week, A taxonomy for next-generation reasoning models, which you can also watch on the AI Engineer World’s Fair page within the full track. My talk was one of 7 or 8 across the full day, which was very enjoyable to be at, so I am honored to have won “best speaker” for it.
Three avenues to pursue now that RL works
The optimistic case for scaling current reinforcement learning with verifiable rewards (RLVR) techniques to next-generation language models, and maybe AGI or ASI depending on your religion, rests entirely on RL being able to learn on ever harder tasks. Where current methods are generating 10K-100K tokens per answer for math or code problems during training, the sort of problems people discuss applying next generation RL training to would be 1M-100M tokens per answer. This involves wrapping multiple inference calls, prompts, and interactions with an environment within one episode that the policy is updated against.
The case for optimism around RL working in these new domains is far less clear compared to current training regimes which largely are rewarding the model for how it does on one interaction with the environment — one coding task checked against tests, one math answer, or one information retrieval. RL is not going to magically let us train language models end-to-end that make entire code-bases more efficient, run scientific experiments in the real world, or generate complex strategies. There are major discoveries and infrastructure improvements that…
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How I Write
6 juni 2025 · 6 min
https://www.interconnects.ai/p/how-i-write Lees meer
My experience with my recent years of writing is quite confusing — almost even dissociative. I've never felt like I was a good writer and no one really told me I was until some random point in time a year or two ago. In that time span, I didn't really change my motivation nor methods, but I reaped the simple rewards of practice. I'm still wired to be very surprised when people I respect wholeheartedly endorse me as "writing very well." Despite the disbelief, when I interrogate what I'm doing and producing it is clear that I've become a good writer.
I don't have a serious writing process. Rather, I make writing a priority. When it is time to write, when my brain is ready, I write. Most of the processing of ideas comes from discussions at work, online, and with myself. The writing is a dance of crystallizing your ideas. It is capturing a moment. This post will take me about 45 minutes on my return flight from San Francisco for a talk, after a nap and a sparkling water. This is standard and it's quite refreshing to have nothing else to do.
I'm torn on the future of writing. It's easy to think that with AI no one will learn to write well again, but at the same time the power of writing well is increasing in careers and with the perception overall impact.
The process of becoming good at writing is quite simple. It takes practice. With practice, you can get to a solid enough level to write clear and engaging prose. The path to becoming a good writer has two sequential milestones:
* Finding something you care about. Then you can write about it. The entry level to this is finding something you want to learn more about. The final level is writing about your passions.
* Finding your voice. Then you can write effortlessly.
People spend too long trying to write as an activity without thinking seriously about why they're writing and what they care about. This makes writing feel like a chore.
Finding your voice also unlocks much…
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A taxonomy for next-generation reasoning models
4 juni 2025 · 13 min
https://www.interconnects.ai/p/next-gen-reasoners Lees meer
On Monday of this week we released RewardBench 2, Ai2’s next reward model evaluation and a project I’ve been personally invested in through its whole arc. Read more of my thoughts here.
Tomorrow, I’ll be presenting a version of this post at the AI Engineer World’s Fair Reasoning & RL track. Come tomorrow and say hi if you’re around the next two days!
The first generation of reasoning models brought us inference-time scaling and intrigue in seeing into what can be called the reasoning process of a language model.
The second generation of reasoning models are going to bring us new types of agentic language modeling applications.
The traits and abilities that are needed for agentic models are additive to the first generation, but not present by default. Some of the new abilities that are needed can be bootstrapped with clever prompting, but for the best results we need to be training our reasoning models directly to optimize for planning.
In this post we explain four key aspects of current and next-generation reasoning models:
* Skills: The ability to solve self-contained problems.
* Calibration: The ability to understand the difficulty of a problem and not overthink.
* Strategy: The ability to choose the right high level plan.
* Abstraction: The ability to break down a strategy into solvable chunks.
These are presented in the order that they should be solved to make a progressively more complete reasoning model for complex tasks. Skills then calibration then strategy then abstraction. The first two are native abilities of models on single inference passes when presented with a technical problem and the latter are skills that are needed to build effective agents.
For grounding, recall the popular “time horizon progression” chart from METR:
The models were saturating around GPT 4o in 2024. Unlocking reasoning skills provided the bump through Claude Sonnet 3.7 in 2025. Planning well will be the trait of mode…
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Claude 4 and Anthropic's bet on code
27 mei 2025 · 15 min
https://www.interconnects.ai/p/claude-4-and-anthropics-bet-on-code Lees meer
Claude’s distinctive characteristics are having a best-in-class personality and the ability to effectively perform software engineering tasks. These characteristics both appeared in force with the first version of Claude 3.5 Sonnet — a major breakthrough model at the time and the model that pulled me away from ChatGPT for the longest. That model was released on Jun 20, 2024, and just the other day on May 22nd, 2025, Anthropic released Claude Opus 4 and Claude Sonnet 4. The strengths of these models are the same.
The models serve as an instrument in Anthropic’s bigger goals. The leading AI models alone now are not a product. All the leading providers have Deep Research integrations set up, ChatGPT uses memory and broader context to better serve you, and our coding interactions are leaving the chat window with Claude Code and OpenAI’s Codex.
Where Anthropic’s consumer touchpoints, i.e. chat apps, have been constantly behind ChatGPT, their enterprise and software tools, i.e. Claude Code, have been leading the pack (or relatively much better, i.e. the API). Anthropic is shipping updates to the chat interface, but they feel half-hearted relative to the mass excitement around Claude Code. Claude Code is the agent experience I liked the best over the few I’ve tried in the last 6 months. Claude 4 is built to advance this — in doing so it makes Anthropic’s path narrower yet clearer.
As a reminder, Claude 4 is a hybrid-reasoning model. This means that reasoning can be turned on and off at the click of a button (which is often implemented with a simple prompt at inference time and length-controlled RL at training time — see the Nemotron reasoning model report for more on hybrid-reasoning techniques). In the future extended thinking could become a tool that all models call to let them think harder about a problem, but for now the extended thinking budget button offers a softer change than switching from GPT…
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People use AI more than you think
21 mei 2025 · 9 min
https://www.interconnects.ai/p/people-use-ai-more-than-you-think Lees meer
I was on ChinaTalk again recently to talk through some of my recent pieces and their corresponding happenings in AI.
Usage and revenue growth for most AI services, especially inference APIs, has been growing like mad for a long time. These APIs have been very profitable for companies — up to 75% or higher margins at times according to Dylan Patel of SemiAnalysis. This is one of those open facts that has been known among the people building AI that can be lost to the broader public in the chorus of new releases and capabilities excitement.
I expect the subscription services are profitable too on the average user, but power users likely are costs to the AI companies alongside the obvious capital expenditures of training frontier models. Still, even if the models were held constant, the usage is growing exponentially and a lot of it is in the realm of profitability.
The extreme, and in some cases exponential, growth in use of AI has been happening well before lots of the incredible progress we’ve seen across the industry in the first half of the year. Reasoning models that change inference answers from something on the order of 100s of tokens to sometimes 10s of thousands of tokens will make the plots of usage even more stark. At the same time, these models are often billed per token so that’ll all result in more revenue.
On top of the industry’s vast excitement and progress in 2025, the Google I/O keynote yesterday was a great “State of the Union” for AI that highlighted this across modalities, form factors, and tasks. It is really recommended viewing. Google is trying to compete on every front. They’re positioned to win a couple use-cases and be in the top 3 of the rest. No other AI company is close to this — we’ll see how their product culture can adapt.
Highlights from I/O include Google’s equivalent product relative to OpenAI’s o1 Pro, Gemini Deep Think, Google’s new multimodal models such as …
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My path into AI
14 mei 2025 · 15 min
https://www.interconnects.ai/p/how-i-got-here Lees meer
Some longer housekeeping notes this week:
* I wrote briefly about a new open-source license, OpenMDW from the Linux Foundation, that seems very solid!
* OpenAI launched the Reinforcement Finetuning (RFT) API. I think my take from when it was teased still holds up super well, you should read it if you haven’t:
* In June, I’ll be speaking at some events in SF and Seattle, I’m looking forward to seeing some readers there. Talk topics are tentative:
* AI Engineer World’s Fair in SF June 3-5 on what we can take away from the last 6 months of reinforcement learning with verifiable rewards (RLVR).
* Enterprise AI Agents in Action in Seattle on June 13 on the art of training a well crafted model.
* VentureBeat Transform in SF on June 24-25 on progress in RL with open source AI.
During the SF trips I’m excited to catch up with old and new friends training and using the latest AI models, so don’t be shy to shoot me an email. Onto the post!
One of the big upsides for my current writing habit is that I should become known by AI models within a couple years. While not offering any immediate technical value in how I use AI, it provides obvious upsides on growing an online presence and fulfilling a very basic human urge for legacy in a way that avoids most personal or moral sacrifice. Other thinkers I follow closely have begun to follow Tyler Cowen's lead on explicitly writing for the AIs and filling in gaps they won't know via what is currently digitized.
I'm joining in and will use it to help push out the limits of my writing. These will build on my two popular job search posts and others like "what it’s like to work in AI right now".
The most defining feature of my young career has been how I prioritize different aspects of work. The work I do today takes on a simple form, but prior to getting to this sustainable place it was more of a striving to belong than a plan to execute.
Getting into AI
Without retelling my enti…
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What people get wrong about the leading Chinese open models: Adoption and censorship
6 mei 2025 · 8 min
https://www.interconnects.ai/p/what-people-get-wrong-about-the-leading Lees meer
Two editor’s notes to start.
* First, we released our OLMo 2 1B model last week and it’s competitive with Gemmas and Llamas of comparable size — I wrote some reflections on training it here.
* Second, my Qwen 3 post had an important factual error — Qwen actually did not release the base models for their 32B and large MoE model. This has important ramifications for research. Onto the update.
People vastly underestimate the number of companies that cannot use Qwen and DeepSeek open models because they come from China. This includes on-premise solutions. Chinese open models are leading in every area when it comes to performance, but translating that to adoption in Western economies is a different story.
Even with the most permissive licenses, there’s a great reluctance to deploy these models into enterprise solutions, even if experimentation is encouraged. While tons of cloud providers raced to host the models on their API services, much fewer than expected entities are actually building with them and their equivalent weights.
The primary concern seems to be the information hazards of indirect influence of Chinese values on Western business systems. With the tenuous geopolitical system this is logical from a high-level perspective, but hard for technically focused researchers and engineers to accept — myself included.
My thinking used to be more aligned with this X user:
it's like having a pen on ur desk but refusing to use it cuz it was made in china
The knee-jerk reaction of the techno-optimist misses the context by which AI models exist. Their interface of language is in its nature immersed in the immeasurable. Why would many companies avoid Chinese models when it’s just a fancy list of numbers and we have no evidence of PRC tampering? A lack of proof.
It’s not the security of the Chinese open models that is feared, but the outputs themselves.
There’s no way, without releasing the tra…
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State of play of AI progress (and related brakes on an intelligence explosion)
30 april 2025 · 19 min
https://www.interconnects.ai/p/brakes-on-an-intelligence-explosion Lees meer
Intelligence explosions are far from a new idea in the technological discourse. They’re a natural thought experiment that follows from the question: What if progress keeps going?
From Wikipedia:
The technological singularity—or simply the singularity—is a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable consequences for human civilization. According to the most popular version of the singularity hypothesis, I. J. Good's intelligence explosion model of 1965, an upgradable intelligent agent could eventually enter a positive feedback loop of successive self-improvement cycles; more intelligent generations would appear more and more rapidly, causing a rapid increase ("explosion") in intelligence which would culminate in a powerful superintelligence, far surpassing all human intelligence.
Given the recent progress in AI, it’s understandable to revisit these ideas. With the local constraints governing decisions within labs, if you extrapolate them, the natural conclusion is an explosion.
Daniel Kokotajlo et al.’s AI 2027 forecast is far from a simple forecast of what happens without constraints. It’s a well thought out exercise on forecasting that rests on a few key assumptions of AI research progress accelerating due to improvements in extremely strong coding agents that mature into research agents with better experimental understanding. The core idea here is that these stronger AI models enable AI progress to change from 2x speed all the way up to 100x speed in the next few years. This number includes experiment time — i.e., the time to train the AIs — not just implementation time.
This is very unlikely. This forecast came at a good time for a summary of many ways the AI industry is evolving. What does it mean for AI as a technology to mature? How is AI research changing? What can we expect in a few years?
In summary, AI …
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Transparency and (shifting) priority stacks
28 april 2025 · 14 min
https://www.interconnects.ai/p/transparency-and-shifting-priority Lees meer
The fact that we get new AI model launches from multiple labs detailing their performance on complex and shared benchmarks is an anomaly in the history of technology products. Getting such clear ways to compare similar software products is not normal. It goes back to AI’s roots as a research field and growing pains into something else. Ever since ChatGPT’s release, AI has been transitioning from a research-driven field to a product-driven field.
We had another example of the direction this is going just last week. OpenAI launched their latest model on a Friday with minimal official documentation and a bunch of confirmations on social media. Here’s what Sam Altman said:
Officially, there are “release notes,” but these aren’t very helpful.
We’re making additional improvements to GPT-4o, optimizing when it saves memories and enhancing problem-solving capabilities for STEM. We’ve also made subtle changes to the way it responds, making it more proactive and better at guiding conversations toward productive outcomes. We think these updates help GPT-4o feel more intuitive and effective across a variety of tasks–we hope you agree!
Another way of reading this is that the general capabilities of the model, i.e. traditional academic benchmarks, didn’t shift much, but internal evaluations such as user retention improved notably.
Of course, technology companies do this all the time. Google is famous for A/B testing to find the perfect button, and we can be sure Meta is constantly improving their algorithms to maximize user retention and advertisement targeting. This sort of lack of transparency from OpenAI is only surprising because the field of AI has been different.
AI has been different in its operation, not only because of its unusually fast transition from research to product, but also because many key leaders thought AI was different. AI was the crucial technology that we needed to get right. This is…
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OpenAI's o3: Over-optimization is back and weirder than ever
19 april 2025 · 11 min
https://www.interconnects.ai/p/openais-o3-over-optimization-is-back Lees meer
Over-optimization is a classic problem to reinforcement learning (RL) proper, the RL from human feedback (RLHF) that gave us ChatGPT, and now what we’re seeing with new reasoning models. All of these have a distinct flavor and different impacts.
Over-optimization is what happens when the optimizer is stronger than the environment or reward function it’s using to learn. The optimizer finds bugs or lapses in the context of its training and produces unusual or negative results.
One of my first pieces of work that got substantial engagement online was from when we used hyperparameter optimization with model-based RL to over-optimize the traditional Mujoco simulation environments used for evaluation of deep RL algorithms. The result was a cartwheeling half-cheetah maximizing its forward velocity, where it was supposed to learn to run. The video is below.
Over-optimization in classical RL made it so that we couldn’t trust the agents to generalize to new tasks and applied great pressure on reward design.
Over-optimization in RLHF made the models completely lobotomized — I mean, repeating random tokens and gibberish, not the misdesign that results in over-refusal — the signal we were overcooking is mismatched from the objective we want. While we can’t know the true objective exactly, we can know when over-optimization occurs.
OpenAI’s new o3 model represents an entirely new type of inference behavior that mirrors a new type of over-optimization.
o3 is an interesting model because of on top of the new improved text-only capabilities, which results in super strong benchmark scores, it also has been designed for multi-step tool use to be used on any query where it’s relevant. It makes OpenAI’s product headaches in ChatGPT way worse — the model will search on its own even if I don’t trigger the search toggle — but comes with a new era in how language models are used.
For example, in the process of writ…
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OpenAI's GPT-4.1 and separating the API from ChatGPT
14 april 2025 · 7 min
https://www.interconnects.ai/p/openais-gpt-41-and-separating-the Lees meer
Recently I gave another talk on RLVR experiments and I posted some thoughts on OLMoTrace — Ai2’s recent tool to let you look at the training data of OLMo 2.
OpenAI has been making many small updates toward their vision of ChatGPT as a monolithic app separate from their API business. Last week OpenAI improved the ChatGPT memory feature — making it so the app can reference the text of previous chats in addition to basic facts about the user. Today, OpenAI announced a new suite of API-only models, GPT 4.1, which is very directly in competition with Google’s Gemini models.
Individually, none of OpenAI’s recent releases are particularly frontier-shifting — comparable performance per dollar models exist — but together they paint a picture of where OpenAI’s incentives are heading. This is the same company that recently teased that it has hit 1 billion weekly active users. This is the company that needs to treat ChatGPT and the models that power it very differently from any other AI product on the market. The other leading AI products are all for coding or information, where personality, vibes, and entertainment are not placed on as high a premium.
A prime example of this shift is that GPT-4.5 is being deprecated from the API (with its extreme pricing), but is going to remain in ChatGPT — where Sam Atlman has repeatedly said he’s blown away by how much users love it. I use it all the time, it’s an interesting and consistent model.
Among their major model releases, such as o3, o4, or the forthcoming open model release, it can be hard to reinforce the high-level view and see where OpenAI is going.
A quick summary of the model performance comes from this chart that OpenAI released in the live stream (and blog post):
Chart crimes aside (using MMLU as y-axis in 2025, no measure of latency, no axis labels), the story from OpenAI is the simple takeaway — better models at faster inference speeds, which are pro…
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Llama 4: Did Meta just push the panic button?
7 april 2025 · 11 min
https://www.interconnects.ai/p/llama-4 Lees meer
Where Llama 2’s and Llama 3’s releases were arguably some of the top few events in AI for their respective release years, Llama 4 feels entirely lost. Meta has attempted to reinvent their formula of models with substantial changes in size, architecture, and personality, but a coherent narrative is lacking. Meta has fallen into the trap of taking too long to ship, so the bar is impossible to cross successfully.
Looking back at the history of Meta’s major open models, the sequence is as follows:
* OPT – Released May 3, 2022 (ai.meta.com | 125M, 350M, 1.3B, 2.7B, 6.7B, 13B, 30B, 66B, 175B): A foundational open model that is underrated in the arc of language modeling research.
* LLaMA – Released February 24, 2023 (ai.meta.com | 7B, 13B, 33B, 65B): The open weight model that powered the Alpaca age of early open chat models.
* Llama 2 – Released July 18, 2023 (our coverage | about.fb.com | 7B, 13B, 70B): The open standard for academic research for its time period. Chat version had some bumps, but overall a major win.
* Llama 3 – Released April 18, 2024 (our coverage | ai.meta.com | 8B, 70B): The open standard for its time. Again, fantastic base models.
* Llama 3.1 – Released July 23, 2024 (our coverage | ai.meta.com | 8B, 70B, 405B): Much improved post training and the 405B marked the first time an open weight model competed with GPT-4!
* Llama 3.2 – Released September 25, 2024 (our coverage | ai.meta.com | 1B, 3B, 11B, 90B): A weird, very underperforming vision release, outshined by Molmo on the same day.
* Llama 3.3 – Released December 6, 2024 (github.com | 70B): Much improved post-training of the smaller 3.1 models, likely in response to other open releases, but largely a minor update.
* Llama 4 – Released April 5, 2025 (ai.meta.com | 17A109B, 17A400B): What we got today.
The time between major versions is growing, and the number of releases seen as exceptional by the community is dropping. Llama 4 consists of 3 models…
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RL backlog: OpenAI's many RLs, clarifying distillation, and latent reasoning
5 april 2025 · 16 min
https://www.interconnects.ai/p/rl-backlog-openais-many-rls-clarifying Lees meer
I have a second blog where I post half-baked thoughts, sometimes previews of what comes here. If you’re interested, I posted some musings on OpenAI’s coming open model release.
It’s obvious that reinforcement learning (RL) is having a total return to glory among the broader AI community, but its real successes are mostly the things people aren’t focusing on. More math and code datasets are important platforms — we know they’re coming and are important. They’re still over-indexed on. The same RL methods are being used in many of the leading models and AI products.
This is largely a post I wrote a few weeks ago on RL news, which I was following. It never had a focusing function, so it didn’t get published, but I’m sharing it because many folks are following this area very closely. Today:
* OpenAI’s many forms of RL,
* On distilling chain of thoughts vs. RL,
* Did DeepSeek distill o1?, and
* Why latent reasoning is so interesting.
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OpenAI’s many forms of RL
For those plugged into the OpenAI cultural tap that is Twitter, it is obvious that they’re very invested in reinforcement learning. With the hype around the release of their o-series of reasoning models, it was easy to assume that those were the only avenue for excitement. OpenAI’s recent releases have shown this is not the case, and every release from a model launch to a new product has included mentions of RL training. Some of this, of course, is marketing, but they all fit as different applications of reinforcement finetuning (RFT) / RL with verifiable rewards (RLVR).
The first other application was OpenAI’s Operator agent. They stated:
Combining GPT-4o's vision capabilities with advanced reasoning through reinforcement learning, CUA is trained to interact with graphical user interfaces (GUIs)—the buttons, menus, and text fields people see on a screen.
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Gemini 2.5 Pro and Google's second chance with AI
26 maart 2025 · 12 min
https://www.interconnects.ai/p/gemini-25-pro-googles-second-ai-chance Lees meer
Google, with its immense infrastructure and talent, has been the safe bet for the question of “Who will have the best models in a few years?” Google took a long time to get here, overcoming Bard’s launch and some integration headaches, and yet the model they launched today, Gemini 2.5 Pro feels like the biggest jump in evaluation scores we’ve seen in quite some time.
It’s often hard to communicate how the models we are getting these days are actually better. To be informed, you need to take a balanced view across many benchmarks, look roughly at the percentage by which the model is clearly state-of-the-art, and of course, try the model yourself.
To summarize, while more evaluations are rolling in, Gemini 2.5 Pro is 40+ Elo points clear on the popular ChatBotArena / LM Arena benchmark (more here). Normally, when a model launches and claims the top spot, it’s barely ahead. In fact, this is the second biggest jump of the top model in LMSYS history, only behind the GPT-4 Turbo overtaking Claude 1. GPT-4 Turbo is when models were not really trained for the benchmark, so progress was much faster.
The blog post highlights insane scores on the benchmarks used to evaluate the leading reasoning models. One to note here is the score of 18.8 on Humanity’s Last Exam without search or tools, which was one of the evaluations I highlighted as impressive with the launch of OpenAI’s Deep Research, which compiles knowledge from the web!
Gemini 2.5 is topping other independent evaluations such as the Scale Leaderboard (which is underrated or at least low on visibility, more here). More independent evaluations are going to trickle in, but all of the ones I’ve seen are extremely positive.
Gemini still is also the model with the longest context length and with very strong multimodal performance (including audio). There are plenty of small wins that Google has like this that are hard to see when skimming the bench…
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Managing frontier model training organizations (or teams)
19 maart 2025 · 13 min
https://www.interconnects.ai/p/how-to-manage-ai-training-organizations Lees meer
It is a closely guarded secret how the leading AI laboratories structure their training teams. As with other technology companies, the saying “you ship your org chart” still applies to training AI models. Looking at these organizational structures will reveal where research can be scaled up, the upper limits of size, and potentially even who uses the most compute.
How modeling teams do and do not work
A crucial area I’m working on (reach out if you would like to share more off the record) is how to scale these lessons to bigger, more complex teams. The core factor differentiating teams that succeed from those that do not is maintaining these principles while scaling team size.
Big teams inherently lead to politics and protecting territory, while language models need information to flow from the bottom to the top on what capabilities are possible. Regardless of the possibilities, leadership can shift resources to prioritize certain areas, but all of the signals on whether this is working come from those training models. If senior directors mandate results under them before unblocking model releases, the entire system will crumble.
Seeing this potential end state — without naming specific companies — it is obviously desirable to avoid, but anticipating and avoiding it during rapid growth takes substantial intentionality.
Within training, the planning for pretraining and post-training traditionally could be managed differently. Pretraining has fewer, bigger runs so improvements must be slotted in for those few annual runs. Post-training improvements can largely be continuous. These operational differences, on top of the obvious cost differences, also make post-training far more approachable for non-frontier labs (though still extremely hard).
Both teams have bottlenecks where improvements must be integrated. Scaling the pretraining bottlenecks — i.e. those making the final architecture and d…
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Gemma 3, OLMo 2 32B, and the growing potential of open-source AI
13 maart 2025 · 14 min
Post: https://www.interconnects.ai/p/gemma-3-olmo-2-32b-and-the-growing Lees meer
Ever since the release of the original ChatGPT, much has been said about making a truly open-source version of it — with data, code, weights, etc., all available. Open-source versions increase transparency, access, long-term progress, security research, and lots more. Lots of people have used this claim to bring hype into their projects, but the substance of these releases have been rather shallow (i.e., often focusing on one evaluation).
This milestone was so long coming that I entirely forgot about it as a target. Through 2024, and especially before DeepSeek, the impression was that scaling AI capabilities was just too expensive for the smaller players willing to do truly open-source development.
Truly open releases take a lot of effort by making more to release and maintain, open up potential legal risks that preclude types of training data, and completely undermine competition. The few organizations doing fully open-source research are non-profits, like Ai2 or Eleuther AI; academics, like LLM360; or companies that benefit from the long-term ecosystem growth, like HuggingFace.
I was poking through the results for our latest model when I realized that we finally did it! We have a fully open-source GPT-4 class model, i.e., it is comparable with OpenAI's original release rather than the current version.
Today, we're releasing OLMo 2 32B, the biggest model we've trained from scratch yet. Here are the post-training evaluations, where it surpasses GPT-3.5, GPT-4o-mini, Qwen 2.5 32B Instruct, the recent Mistral Small 24B, and comes close to the Qwen and Llama 70B Instruct models.
And this recipe is extremely training efficient. Here’s a plot showing the FLOP comparisons to peer base models:
Most of this release isn't entirely new. OLMo 2 is the result of lots of small wins on data, architecture, post-training with Tülu 3 recipe and so on — we just let the GPUs hum for a lot longer. You can l…
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Interviewing Eugene Vinitsky on self-play for self-driving and what else people do with RL
12 maart 2025 · 1 uur 9 min
Eugene Vinitsky is a professor a New York University department of Civil and Urban Engineering. He’s one of my original reinforcement learning friends from when we were both doing our Ph.D.’s in RL at UC Berkeley circa 2020. Eugene has extensive experience in self-driving, open endedness, multi-agent reinforcement learning, and self-play with RL. In this conversation we focus on a few key topics: Lees meer
* His latest results on self-play for self-driving and what they say about the future of RL,
* Why self-play is confusing and how it relates to the recent takeoff of RL for language models, and
* The future of RL in LMs and elsewhere.
This is a conversation where we take the time to distill very cutting edge research directions down into the core essences. I felt like we were learning in real time what recent developments mean for RL, how RL has different scaling laws for deep learning, and what is truly salient about self-play.
The main breakthrough we discuss is scaling up self-play techniques for large-scale, simulated reinforcement learning. Previously, scaling RL in simulation has become economical in single-agent domains. Now, the door is open to complex, multi-agent scenarios where more diversity is needed to find solutions (in this case, that’s what self play does).
Eugene’s Google Scholar | Research Lab | Linkedin | Twitter | BlueSky | Blog (with some great career advice).
Listen on Apple Podcasts, Spotify, YouTube, and where ever you get your podcasts. For other Interconnects interviews, go here.
Show outline & links
We cover many papers in this podcast. Also, as an experiment, here’s a Deep Research report on “all the papers that appeared in this podcast transcript.”
In this episode, we cover:
* Self-play for self-driving, mostly around the recent paper Robust Autonomy Emerges from Self-Play (Cusumano-Towner et al. 2025). The simulator they built powering this is Gigaflow. More discussion on HackerNews.(Here’s another self-play for self-driving paper …
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Elicitation, the simplest way to understand post-training
10 maart 2025 · 8 min
Full post: https://www.interconnects.ai/p/elicitation-theory-of-post-training Lees meer
If you look at most of the models we've received from OpenAI, Anthropic, and Google in the last 18 months, you'll hear a lot of "Most of the improvements were in the post-training phase." The most recent one was Anthropic’s CEO Dario Amodei explaining Claude 3.7 on the Hard Fork Podcast:
We are not too far away from releasing a model that's a bigger base model. Most of the improvements in 3.6/3.7 are in the post-training phase. We're working on stronger base models (perhaps that will be the Claude 4 series, perhaps not; those are coming in a relatively small number of time units [months?].
Here's a simple analogy for how so many gains can be made on mostly the same base model.
The intuition I've been using to understand the potential of post-training is called the elicitation interpretation of post-training, where all we are doing is extracting and amplifying valuable behaviors in the base model.
Consider Formula 1 (F1), most of the teams show up to the beginning of the year with a new chassis and engine. Then, they spend all year on aerodynamics and systems changes (of course, it is a minor oversimplification), and can dramatically improve the performance of the car. The best F1 teams improve way more during a season than chassis-to-chassis.
The same is true for post-training. The best post-training teams extract a ton of performance in a very short time frame. The set of techniques is everything after the end of most of pretraining. It includes "mid-training" like annealing / high-quality end of pre-training web data, instruction tuning, RLVR, preference-tuning, etc. A good example is our change from the first version of OLMoE Instruct to the second — we improved our post-training evaluation average from 35 to 48 without touching the majority of pretraining.
Then, when you look at models such as GPT-4.5, you can see this as a way more dynamic and exciting base for OpenAI to build…
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Where inference-time scaling pushes the market for AI companies
5 maart 2025 · 14 min
Link: https://www.interconnects.ai/p/where-inference-time-scaling-pushes Lees meer
There’s a lot of noise about the current costs of AI models served for free users, mostly saying it’s unsustainable and making the space narrow for those with the historical perspective of costs of technology always plummeting. GPT-4.5’s odd release of a “giant” model without a clear niche only amplified these critics. With inference-time compute being a new default mode, can we still have free AI products? Are we just in the VC-subsidized era of AI?
For normal queries to ChatGPT, the realistic expectation is that the cost of serving an average query will drop to be extremely close to zero, and the revenue from a future ad model will make the service extremely profitable. The most cohesive framework for understanding large-scale internet businesses built on the back of such zero marginal costs is Ben Thompson’s Aggregation Theory.
Aggregation Theory posits that extreme long-term value will accrue to the few providers that gate access to information and services built on zero-marginal cost dynamics. These companies aggregate user demand. It has been the mode of modern dominant businesses, with the likes of Google and Meta producing extremely profitable products. Naturally, many want to study how this will apply to new AI businesses that are software-heavy, user-facing platforms, of which OpenAI is the most prominent due to the size of ChatGPT. Having more users and attention enables aggregators to better monetize interactions and invest in providing better experiences, a feedback loop that often compounds.
Aggregators are often compared to platforms. Where the former relies on being an intermediary of users and other marketplaces, platforms serve as foundations by which others build businesses and value, such as Apple with the iPhone, AWS, or Stripe.
Businesses like ChatGPT or Perplexity will rely on a profitable advertisement serving model being discovered that works nicely for the dialog…
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GPT-4.5: "Not a frontier model"?
28 februari 2025 · 10 min
More: https://www.interconnects.ai/p/gpt-45-not-a-frontier-model Lees meer
As GPT-4.5 was being released, the first material the public got access to was OpenAI’s system card for the model that details some capability evaluations and mostly safety estimates. Before the live stream and official blog post, we knew things were going to be weird because of this line:
GPT-4.5 is not a frontier model.
The updated system card in the launch blog post does not have this. Here’s the original system card if you need a reference:
Regardless, someone at OpenAI felt the need to put that in. The peculiarity here summarizes a lot of the release. Some questions are still really not answered, like “Why did OpenAI release this?” That game theory is not in my purview.
The main contradiction to the claims that it isn’t a frontier model is that this is the biggest model the general public has ever gotten to test. Scaling to this size of model did NOT make a clear jump in capabilities we are measuring. To summarize the arc of history, the jump from GPT-3.5 to GPT-4 made the experience with the models go from okay to good. The jump from GPT-4o (where we are now) to GPT-4.5 made the models go from great to really great.
Feeling out the differences in the latest models is so hard that many who are deeply invested and excited by AI’s progress are just as likely to lie to themselves about the model being better as they are to perceive real, substantive improvements. In this vein, I almost feel like I need to issue a mea culpa. I expected this round of scaling’s impacts to still be obvious before the brutal economic trade-offs of scaling kicked in.
While we got this model, Anthropic has also unintentionally confirmed that their next models will be trained on an approximation of “10X the compute,” via a correction on Ethan Mollick’s post on Claude 3.7.
Note: After publishing this piece, I was contacted by Anthropic who told me that Sonnet 3.7 would not be considered a 10^26 FLOP model and cost a …
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Character training: Understanding and crafting a language model's personality
26 februari 2025 · 12 min
https://www.interconnects.ai/p/character-training Lees meer
The vast majority of evaluations used to measure progress on post-training at frontier laboratories are internal evaluations rather than the evaluations you hear about all the time like MATH or GPQA. These, the well-known intra-industry evaluations, are certainly important for ballparking behavior, but for every public evaluation, these frontier laboratories are likely to have 10+ fine-grained internal evaluations.
The internal evaluations these model providers have cover a range of topics. Surely, most of them are basic, repetitive user behaviors that they need to make sure a new model doesn’t roll back too many of. Of these, the vast majority are likely skills, and “character” remains more of an art than a hill to climb up with careful data engineering.
Leading post-training laboratories surely know how to reinforce more robust behavior within a specific character, as seen by the march of progress on evaluations like ChatBotArena, but crafting a specific personality from scratch is an open question.
The primary goal of this post is to start the conversation outside of frontier AI labs around character training. Character training is the subset of post-training designed around crafting traits within the model in the manner of its response, rather than the content. Character training, while being important to the user experience within language model chatbots, is effectively non-existent on the web.
We don’t know the trade-offs of what character training does, we don’t know how exactly to study it, we don’t know how much it can improve user preferences on ChatBotArena, and we should.
The appearance of the AIs people are using is deeply coupled with how intelligent users will find it to be. Style of communication is crucial to how information is parsed. This is likely a very high priority to industrial labs, but something that almost no academic literature exists on. Even though I want to do research on this, I…
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Claude 3.7 thonks and what's next for inference-time scaling
24 februari 2025 · 10 min
On Monday, February 24th, 2025, Anthropic announced their latest model, Claude 3.7 Sonnet, which is their first model explicitly trained to use more inference time tokens to improve performance. This is another reinforcement learning (RL) trained model (mentioned in system card). With this model, they also released Claude Code as a limited research preview, which is a “command line tool for agentic coding.” Continuous improvements in models are enabling new modalities and domains addressable by the models, but assessing the impact of each new domain takes far more time than a quick model reaction post. Lees meer
This is a tidy release, a solid improvement, but not a step change for Claude or the industry. Expect a lot of small changes to accumulate massively this year.
Claude 3.7 Sonnet is a clear improvement over Claude 3.5 Sonnet (New) and continues to push the limits in areas where users love Claude (e.g. read Ethan Mollick’s review here). The scores for those areas such as software development (SWE Bench) and tool use, are clearly state-of-the-art.
For example, Claude 3.7 Sonnet is the highest scoring “standard non-reasoning” language model on the Aider Polyglot benchmark. While models like o3 and Grok 3 DeepThink highlight superhuman performance on code benchmarks, this sort of behavior being integrated without extra inference time compute is wonderful. The price for superhuman coding AI is plummeting.
Even with o1 Pro, I still find myself using Claude 3.5 (New) on a regular basis. O1 Pro is the best model for doing succinct, one-off tasks like writing short scripts. It is extremely controllable and will often work out of the box. Though, when I’m doing tricky, iterative tasks I still use Claude. Claude 3.7 Sonnet only makes these workflows stronger and I’m stoked to play with it further.
The most useful piece of this release for those trying to understand the direction of the ecosystem, rather than just the status of today, is Anthropic’s post on Claude’s extendin…
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Grok 3 and an accelerating AI roadmap
18 februari 2025 · 12 min
Full post: https://www.interconnects.ai/p/grok-3-and-an-accelerating-ai-roadmap Lees meer
xAI launched their latest flagship model, Grok 3, last night via a live stream on X, which is a new take on the launch process, but it largely felt familiar. Grok 3 is a state-of-the-art model on some important benchmarks. The core is that it is state-of-the-art relative to available models and we know better models are out there. Only some of them have been announced, some of them have been teased, and others lie in waiting.
What feels different is how the broader AI industry is signaling rapid progress coming soon. xAI said on the livestream that they will be updating the model “daily.” An era of sitting on unreleased models could be ending.
Grok 3’s release is a reinforcement of trends people began reckoning with as of the release of DeepSeek V3 + R1 — AI progress is not held in the hands of a few companies nor is it slowing down. 2023 and 2024 were defined by truly state-of-the-art AI being concentrated within OpenAI, Anthropic, and Google, where these companies could take a lot of time to package models from training to release and still have a substantial moat on capabilities relative to their peers.
At the time of R1’s launch, the “people’s choice” model was Claude 3.5 Sonnet, a model that had been trained “9-12 months ago” and the best models like Claude 3.5 Opus or GPT-4.5 (a.k.a Orion) were not available to users for a grab bag of reasons.
Competitive pressure from DeepSeek and Grok integrated into a shifting political environment for AI — both domestic and international — will make the established leading labs ship sooner. A large portion of delays in delivering models is for “safety testing,” but we don’t have exact details on how much of it was that and how much was cost-benefit tradeoffs (and other big company hurdles such as legal departments). The brand, and culture, of “having the smartest model” is extremely important to these companies, but having a way smarter m…
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An unexpected RL Renaissance
13 februari 2025 · 40 min
The era we are living through in language modeling research is one characterized by complete faith that reasoning and new reinforcement learning (RL) training methods will work. This is well-founded. A day | cannot | go | by | without | a new | reasoning model, RL training result, or dataset distilled from DeepSeek R1. Lees meer
The difference, compared to the last time RL was at the forefront of the AI world with the fact that reinforcement learning from human feedback (RLHF) was needed to create ChatGPT, is that we have way better infrastructure than our first time through this. People are already successfully using TRL, OpenRLHF, veRL, and of course, Open Instruct (our tools for Tülu 3/OLMo) to train models like this.
When models such as Alpaca, Vicuña, Dolly, etc. were coming out they were all built on basic instruction tuning. Even though RLHF was the motivation of these experiments, tooling, and lack of datasets made complete and substantive replications rare. On top of that, every organization was trying to recalibrate its AI strategy for the second time in 6 months. The reaction and excitement of Stable Diffusion was all but overwritten by ChatGPT.
This time is different. With reasoning models, everyone already has raised money for their AI companies, open-source tooling for RLHF exists and is stable, and everyone is already feeling the AGI.
Aside: For a history of what happened in the Alpaca era of open instruct models, watch my recap lecture here — it’s one of my favorite talks in the last few years.
The goal of this talk is to try and make sense of the story that is unfolding today:
* Given it is becoming obvious that RL with verifiable rewards works on old models — why did the AI community sleep on the potential of these reasoning models?
* How to contextualize the development of RLHF techniques with the new types of RL training?
* What is the future of post-training? How far can we scale RL?
* How does today’s RL compare to historical successes of Deep …
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Deep Research, information vs. insight, and the nature of science
12 februari 2025 · 14 min
Article: https://www.interconnects.ai/p/deep-research-information-vs-insight-in-science Lees meer
(sorry about some more audible breaths in this -- I'm going to work on it!)
We at Ai2 released a local LM iPhone app for our OLMoE model (1B active, 7B total params), with greatly improved scores! Let us know what you think, or read more here.
OpenAI’s Deep Research has largely been accepted as a super valuable tool for knowledge workers and analysts across the economy, but its real engine of economic progress is going to be changing the nature of scientific progress. Science is the fuel of technological revolutions.
Deep Research in its current form feels like a beta version of a next-generation piece of technology. It does what it is tasked with — searches the web and processes many resources to create a useful report with referenced sources. Some of my uses include researching model evaluations, recent robotic learning research, and AI for science breakthroughs.
Deep Research’s limitations mostly feel like problems of search, where it is prone to returning SEO optimized slop, style, where it returns verbose, low information density writing, and modality, where it does not have the ability to read, process, and return plots and diagrams. All of these are surely solvable and expected features if we look at the rollouts of other AI models in the last few years.
This isn’t a product review (you can read Stratechery or Turing Post for more of that) — as the answer is quite simple, if you work in a knowledge intensive vocation you should be using this — but rather asking: So what comes next?
The place to start from within AI circles is to revisit the question of “When will AI make novel discoveries?” A good example of this is in the Dwarkesh Podcast episode with Dario Amodei:
One question I had for you while we were talking about the intelligence stuff was, as a scientist yourself, what do you make of the fact that these things have basically the entire corpus of human know…
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Making the U.S. the home for open-source AI
5 februari 2025 · 16 min
As many of you know, this weekend I appeared on the Lex Fridman Podcast with my friend Dylan Patel of SemiAnalysis to cover DeepSeek and the implications on the AI ecosystem. I recommend you check it out. Lees meer
This post was tricky to pull together. I decided to share it anyways given the timeliness of the topic and other more exciting things I have to get to. The minor, thematic contradictions on motivations, costs, and trajectories are exactly indicative of why analysis and productionization of open-source AI is so hard. In that, it is a valuable lesson that building open-source AI will come with a lot of ups and downs, but now is the best time to do so.
The DeepSeek moment represents the end of the first chapter of AI's recent takeoff as told through the emergence of ChatGPT. It reminds us, that while substantial resources, coalitions, brands, and trends have been established, the narratives we have been championing are not set in stone. DeepSeek, especially with R1, resets all the narratives around open vs closed, US vs China, scaling and commoditization, etc. as we prep for yet another acceleration in the diffusion, progress, and adoption of AI.
Of all of these debates, the focus on open vs. closed AI models is the one least driven by economic factors and most driven by vibes. The open-source AI community is driven by a future vision where AI is not held by a few super-rich companies, a future where more people get to partake in the building of AI, a future where AI is safer, etc. These are ideals and building the tools and systems that make this vision a reality is a monumental challenge. Building strong AI models is far, far easier than building a sustainable open-source ecosystem around AI.
Building a better, truly open ecosystem for AI has been my life’s work in the last years and I obviously want it to flourish further, but the closer you are to the core of the current open-source ecosystem, the more you know that is not a given with costs of doing relevant…
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Why reasoning models will generalize
28 januari 2025 · 12 min
This post is early to accommodate some last minute travel on my end! Lees meer
The new models trained to express extended chain of thought are going to generalize outside of their breakthrough domains of code and math. The “reasoning” process of language models that we use today is chain of thought reasoning. We ask the model to work step by step because it helps it manage complexity, especially in domains where the answer requires precision across multiple specific tokens. The domains where chain of thought (CoT) is most useful today are code, mathematics, and other “reasoning” tasks. These are the domains where models like o1, R1, Gemini-Thinking, etc. were designed for.
Different intelligences reason in different ways that correspond to how they store and manipulate information. Humans compress a lifetime of experience into our spectacular, low-power brains that draw on past experience almost magically. The words that follow in this blog are also autoregressive, like the output of a language model, but draw on hours and hours of background processing as I converge on this argument.
Language models, on the other hand, are extremely general and do not today have architectures (or use-cases) that continually re-expose them to relevant problems and fold information back in a compressed form. Language models are very large, sophisticated, parametric probability distributions. All of their knowledge and information processing power is stored in the raw weights. Therein, they need a way of processing information that matches this. Chain of thought is that alignment.
Chain of thought reasoning allows information to be naturally processed in smaller chunks, allowing the large, brute force probability distribution to work one token at a time. Chain of thought, while allowing more compute per important token, also allows the models to store intermediate information in their context window without needing explicit recurrence.
Recurrence is required for reasoning and this can eit…
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Interviewing OLMo 2 leads: Open secrets of training language models
22 januari 2025 · 1 uur 13 min
We're here to share the story of building our Open Language Models (OLMos) and what we improved to build the OLMo 2 7B/13B model that is competitive with the Llama 3.1 8B model. This is all about building an effective, small language modeling team that can share all it learns with the scientific community. Dirk, Luca, and Kyle are some of the people I learn the most from and have more knowledge (and entertainment) to share than we have time. Lees meer
Some questions were pulled from Twitter, but please comment or get in touch if you want us to cover anything in the future episode(s)!
Main topics:
* Pretraining efficiency and our quest for stability after a not-so-secret failed 70B run early in 2024,
* What the role of OLMo is in the broader AI landscape and how that is, or is not, changing,
* Many little decisions that going into building language models and their teams (with a focus on NOT post-training, given I already talk about that a ton).
Play with the models we build here: playground.allenai.org/
For more history of open language models (OLMos) on Interconnects, see my first post on OLMo, my coverage of OLMoE, OLMo 2, and why I build open language models. If you have more questions or requests, please let us know (especially the researchers out there) and this can be one of N, rather than a one off celebration.
Listen on Apple Podcasts, Spotify, YouTube, and where ever you get your podcasts. For other Interconnects interviews, go here.
Contacts
Dirk Groeneveld — https://x.com/mechanicaldirk // https://bsky.app/profile/mechanicaldirk.bsky.social
Kyle Lo — https://x.com/kylelostat // https://bsky.app/profile/kylelo.bsky.social
Luca Soldaini — https://twitter.com/soldni // https://bsky.app/profile/soldaini.net
General OLMo contact — olmo@allenai.org
Papers / models / codebases discussed
* OLMo 2 paper
* OLMo 1 paper
* OPT models and talk from Susan Zhang
* BLOOM
* Red Pajama V1 Dataset
* Falcon LLM
* C4: Boosting Large-scale Parallel Training Efficie…
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DeepSeek R1's recipe to replicate o1 and the future of reasoning LMs
21 januari 2025 · 20 min
Full post for links, images, etc: https://www.interconnects.ai/p/deepseek-r1-recipe-for-o1 Lees meer
I have a few shows to share with you this week:
* On The Retort a week or two ago, we discussed the nature of AI and if it is a science (in the Kuhn’ian sense)
* I appeared on Dean W. Ball and Timothy B. Lee’s new podcast AI Summer to discuss “thinking models” and the border between post-training and reasoning methods. Listen here.
* Finally, a talk I gave at NeurIPs on how I think about post-training for AI applications is now public.
This post is likely getting cut off in email inboxes — I recommend reading online by clicking on the title!
Yesterday, January 20th, China’s open-weights frontier AI laboratory, DeepSeek AI, released their first full fledged reasoning model. It came as:
* A flagship reasoning language model, R1, trained via a 4-stage, RL heavy process. It is MIT-licensed which means companies and researchers can build upon and train on its outputs to accelerate the development and deployment of reasoning language models (RLMs).
* An RL-only reasoning model trained directly from their V3 base model, R1-Zero (used to create training data for full R1).
* A suite of open-weight models finetuned with supervised finetuning (SFT) data derived from R1 (similar data to one of their intermediate training steps).
* A technical report detailing their RL training methods.
* Models are available at chat.deepseek.com (via DeepThink) and in their new app.
This post is less about the evaluation results (which, of course, are extremely good and shown below), but rather about how training is done and what it all means.
This is a major transition point in the uncertainty in reasoning model research. Until now, reasoning models have been a major area of industrial research without a clear seminal paper. Before language models took off, we had the likes of the GPT-2 paper for pretraining or InstructGPT (and Anthropic’s whitepapers) for post-training. For reasoning, we w…
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Let me use my local LMs on Meta Ray-Bans
15 januari 2025 · 10 min
Full post for images, etc: https://www.interconnects.ai/p/to-meta-ray-ban-local-ai Lees meer
With the Rabbit r1, the Humane pin, the Friend thing, the Sam Altman rumors, Meta Ray-Bans, and everything in between, it is obvious that we are going to get new devices in the near future driven by advancements in AI. Trying some of those that already are public makes this obvious from a functional perspective rather than a marketing perspective.
Even though many of these devices will have a shelf life drastically shortened by the underlying API access getting turned off when the parent company runs out of money, the call for these devices is very strong. AI is going to be more than a chat window we use for work, we just don’t know what that will feel like. AI should be fun, flexible, and available.
Meta’s Ray-Bans were first launched in 2021, long before any of this ChatGPT-inspired interest in AI began. Having tried them — the form factor would have caught on eventually, but AI was the catalyst to accelerate adoption. AI expanded our expectations for the range of exciting outcomes that could be coming our way.
Using the AI in the Ray-Bans is much like using a protolithic chatbot. If I had never used ChatGPT, it would have been transformative, but today it feels slightly outdated. We should be more impressed by these generally and contextualize the AI they’re delivering. The product excitement cumulatively feels unexpectedly like what AirPods had on day 1. I was not expecting this fondness.
The form factor for the Meta Ray-Bans is fantastic and drives this connection. I’ve been legitimately excited to use them (albeit, much more during sunny Seattle summers relative to now), and it immediately made sense when taking them out of the packaging. My best use has been for outdoor activities, taking photos and videos without needing to fuss with a phone and communications. An example video is below -- like most things, it has a learning curve.Here’s a photo from that outing:
Or a v…
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(Voiceover) DeepSeek V3 and the actual cost of training frontier AI models
9 januari 2025 · 17 min
Original post: Lees meer
https://www.interconnects.ai/p/deepseek-v3-and-the-actual-cost-of
Chapters
00:00 Opening
03:15 DeepSeek’s learning efficiency
06:49 DeepSeek’s compute transparency and reality
Figures
Fig 1: Benchmark Results
Fig 2: ChatBotArena Results
Fig 3: Compute Usage Table
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.interconnects.ai/subscribe
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The state of post-training in 2025
8 januari 2025 · 54 min
Slides for this post-training talk and slides for the full tutorial on language modeling (with a bit less post-training content and no recording yet). Here are some timestamps for the video: Lees meer
00:00 Introduction 10:00 Prompts & Skill Selection 14:19 Instruction Finetuning 21:45 Preference Finetuning 36:17 Reinforcement Finetuning 45:28 Open Questions 52:02 Wrap Up
Psssst… we just recently released our technical report for OLMo 2 — 2 OLMo 2 Furious, check it out for tons of training details and tips!
This post has some good content, but if you just want to watch the tutorial on YouTube, it’s here.
I’m far more optimistic about the state of open recipes for and knowledge of post-training starting 2025 than I was starting 2024. Last year one of my first posts was how open post-training won’t match the likes of GPT-4. This is still the case, but now we at least understand the scope of things we will be working with better.
It’s a good time to record an overview of what post-training looks like today. I gave a version of this tutorial talk for the first time in 2023 (at ICML), which felt like a review of the InstructGPT paper not based on reproduced literature knowledge. In 2024, the scientific community made substantial progress in actually training these models and expanding the frontier of knowledge. Doing one of these talks every year feels like a good way to keep tabs on the state of play (whereas last year, I just had a bunch of links to add to the conversation on where to start).
With the talk, I wanted to add more context on where I see post-training generally.
The most important one people need to know, given the excitement around OpenAI’s o1 series of models, is that post-training alone is nowhere near a complete enough lens or taxonomy to study training reasoning language models. It’s a step.
Back to processes for all modern AI models. There are a lot of post-training methods to improve models and, more importantly, they can be segmented so the scientif…
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Quick recap on the state of reasoning
2 januari 2025 · 16 min
In 2025 we need to disambiguate three intertwined topics: post-training, reasoning, and inference-time compute. Post-training is going to quickly become muddied with the new Reasoning Language Models (RLMs — is that a good name), given that loss functions that we studied via advancements in post-training are now being leveraged at a large scale to create new types of models. Lees meer
I would not call the reinforcement learning training done for OpenAI’s o1 series of models post-training. Training o1 is large-scale RL that enables better inference-time compute and reasoning performance. Today, I focus on reasoning. Technically, language models definitely do a form of reasoning. This definition does not need to go in the direction of the AGI debate — we can clearly scope a class of behavior rather than a distribution of explicit AI capability milestones. It’ll take work to get an agreement here.
Getting some members of the community (and policymakers) to accept that language models do their own form of reasoning by outputting and manipulating intermediate tokens will take time. I enjoy Ross Taylor’s definition:
Reasoning is the process of drawing conclusions by generating inferences from observations.
This is a talk I gave at NeurIPS at the Latent Space unofficial industry track. I wanted to directly address the question on if language models can reason and what o1 and the reinforcement finetuning (RFT) API tell us about it. It’s somewhat rambly, but asks the high level questions on reasoning that I haven’t written about yet and is a good summary of my coverage on o1’s implementation and the RFT API.
Thanks swyx & Alessio for having me again! You can access the slides here (e.g. if you want to access the links on them). For more on reasoning, I recommend you read/watch:
* Melanie Mitchell’s series on ARC at AI: A Guide for Thinking Humans: first, second, third, and final. And her post on reasoning proper.
* Miles Brundage’s thread summarizing the prospects of generaliz…
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(Voiceover) 2024 Interconnects year in review
31 december 2024 · 6 min
Original post Lees meer
https://www.interconnects.ai/p/2024-interconnects-year-in-review
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