Misha Laskin

Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.

- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch

Full weights release this month.

Learn more about Beam: reflection.ai/beam

Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.

- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch

Full weights release this month.

Learn more about Beam: reflection.ai/beam

Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry.

Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come.

But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility.

This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems.

Together, we are building the foundation of the AI economy.

Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. nvda.ws/4hcoq7m

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.

The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.

The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

Exciting day for NVIDIA and @huggingface.

Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. They allow every developer, startup, university, industry and country to build with, customize and benefit from AI.

Thank you @ClementDelangue for coming to me.

NVIDIA is going to be a great home for Hugging Face, its community and the future of open models. 🤗

blogs.nvidia.com/blog/nvidia-to…

Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community.

During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion.

That’s why we created the Open Secure AI Alliance.

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.

AI will transform every industry, power every company, and be built by every country.

Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.

The world needs both frontier closed models and frontier open models.

images.nvidia.com/pdf/Open-Weigh…

BREAKING: Llama lead @joespeez is joining Reflection as VP of Product & Head of Open Source

Most approaches to “agentic AI” focus on post-training fixes.

In this conversation, member of our technical staff, @achowdhery argues the bottleneck is pre-training itself. Drawing on her work on PaLM and early Gemini, she explains why next-token prediction breaks down for long-horizon planning -- and how objectives, attention, and training data must evolve to support true agentic behavior.

An update: I have left Meta Superintelligence Labs and joined @reflection_ai in NYC!! Today is my first day.

I started in the Fundamental AI Research (FAIR) lab at Meta, then Facebook, over six (!) years ago as my first job out of the PhD. They were some formative years. The group is full of exceptionally talented people that have profoundly shaped my perspective on life and research. I am grateful for everything we have shared and proud of everything we created together.

I have decided it's time to try to build a startup and new frontier models with Reflection. Superintelligence will be one of the most significant advancements of our lifetimes, resulting in a computational reflection of ourselves. We believe it should be safe, open, and accessible to all. I am excited to be jumping into the post-training and reinforcement learning pipelines to advance capabilities and alignment.

And we are hiring! Please get in touch.

Introducing Ricursive Intelligence, a frontier AI lab enabling a recursive self-improvement loop between AI and the chips that fuel it.

Learn more at ricursive.com

Hi friends, after three incredible years at OpenAI I am excited to share that I am starting a new chapter at @reflection_ai, where I will be leading the Science of Scaling team.

Our mission is to deepen the scientific understanding of large scale learning and to turn compute into intelligence as efficiently and predictably as possible.

After three years at CRV, I am stepping onto Striker Venture Partners' founding team, leading the firm's AI investments. Thanks to @BusinessInsider for covering the move.

We present Olmo 3, our next family of fully open, leading language models.
This family of 7B and 32B models represents:

1. The best 32B base model.
2. The best 7B Western thinking & instruct models.
3. The first 32B (or larger) fully open reasoning model.

This is a big milestone for Ai2 and the Olmo project. These aren’t huge models (more on that later), but it’s crucial for the viability of fully open-source models that they are competitive on performance – not just replications of models that came out 6 to 12 months ago. As always, all of our models come with full training data, code, intermediate checkpoints, training logs, and a detailed technical report. All are available today, with some more additions coming before the end of the year.

As with OLMo 2 32B at its release, OLMo 3 32B is the best open-source language model ever released. It’s an awesome privilege to get to provide these models to the broader community researching and understanding what is happening in AI today.

Base models – a strong foundation

Pretraining’s demise is now regularly overstated. 2025 has marked a year where the entire industry rebuilt their training stack to focus on reasoning and agentic tasks, but some established base model sizes haven’t seen a new leading model since @alibaba_qwen's Qwen 2.5 in 2024. The Olmo 3 32B base model could be our most impactful artifact here, as Qwen3 did not release their 32B base model (likely for competitive reasons). We show that our 7B recipe competes with Qwen 3, and the 32B size enables a starting point for strong reasoning models or specialized agents. Our base model’s performance is in the same ballpark as Qwen 2.5, surpassing the likes of Stanford’s Marin (@stanfordAILab) and Gemma 3 (@GoogleDeepMind), but with pretraining data and code available, it should be more accessible to the community to learn how to finetune it (and be confident in our results).

We’re excited to see the community take Olmo 3 32B base in many directions. 32B is a loved size for easy deployment on single 80GB+ memory GPUs and even on many laptops, like the MacBook I’m using to write this on.

A model flow – the lifecycle of creating a model

With these strong base models, we’ve created a variety of post-training checkpoints to showcase the many ways post-training can be done to suit different needs. We’re calling this a “Model Flow.” For post-training, we’re releasing Instruct versions – short, snappy, intelligent, and useful especially for synthetic data en masse (e.g. recent work by Datology @datologyai on OLMo 2 Instruct), Think versions – thoughtful reasoners with the performance you expect from a leading thinking model on math, code, etc. and RL Zero versions – controlled experiments for researchers understanding how to build post-training recipes that start with large-scale RL on the base model.

The first two post-training recipes are distilled from a variety of leading, open and closed, language models. At the 32B and smaller scale, direct distillation with further preference finetuning and reinforcement learning with verifiable rewards (RLVR) is becoming an accessible and highly capable pipeline. Our post-training recipe follows our recent models: 1) create an excellent SFT set, 2) use direct preference optimization (DPO) as a highly iterable, cheap, and stable preference learning method despite its critics, and 3) finish up with scaled up RLVR. All of these stages confer meaningful improvements on the models’ final performance.

Instruct models – low latency workhorse

Instruct models today are often somewhat forgotten, but the likes of @aiatmeta Llama 3.1 Instruct and smaller, concise models are some of the most adopted open models of all time. The instruct models we’re building are a major polishing and evolution of the Tülu 3 pipeline – you’ll see many similar datasets and methods, but with pretty much every datapoint or training code being refreshed. Olmo 3 Instruct should be a clear upgrade on Llama 3.1 8B, representing the best 7B scale model from a Western or American company. As scientists we don’t like to condition the quality of our work based on its geographic origins, but this is a very real consideration to many enterprises looking to open models as a solution for trusted AI deployments with sensitive data.

Building a thinking model

What people have most likely been waiting for are our thinking or reasoning models, both because every company needs to have a reasoning model in 2025, but also to clearly open the black box for the most recent evolution of language models. Olmo 3 Think, particularly the 32B, are flagship models of this release, where we considered what would be best for a reasoning model at every stage of training.

Extensive effort (ask me IRL about more war stories) went into every stage of the post-training of the Think models. We’re impressed by the magnitude of gains that can be achieved in each stage – neither SFT nor RL is all you need at these intermediate model scales.

First we built an extensive reasoning dataset for supervised finetuning (SFT), called Dolci-Think-SFT, building on very impactful open projects like OpenThoughts3, Nvidia’s Nemotron Post-training, Prime Intellect’s SYNETHIC-2, and many more open prompt sources we pulled forward from Tülu 3 / OLMo 2. Datasets like this are often some of our most impactful contributions (see the Tülu 3 dataset as an example in Thinking Machine’s Tinker :D @thinkymachines @tinker_api – please add Dolci-Think-SFT too, and Olmo 3 while you’re at it, the architecture is very similar to Qwen which you have).

For DPO with reasoning, we converged on a very similar method as HuggingFace’s (@huggingface) SmolLM 3 with Qwen3 32B as the chosen model and Qwen3 0.6B as the rejected. Our intuition is that the delta between the chosen and rejected samples is what the model learns from, rather than the overall quality of the chosen answer alone. These two models provide a very consistent delta, which provides way stronger gains than expected. Same goes for the Instruct model. It is likely that DPO is helping the model converge on more stable reasoning strategies and softening the post-SFT model, as seen by large gains even on frontier evaluations such as AIME.

Our DPO approach was an expansion of Geng, Scott, et al. "The delta learning hypothesis: Preference tuning on weak data can yield strong gains." arXiv preprint arXiv:2507.06187 (2025). Many early open thinking models that were also distilled from larger, open-weight thinking models likely left a meaningful amount of performance on the table by not including this stage.

Finally, we turn to the RL stage. Most of the effort here went into building effective infrastructure to be able to run stable experiments with the long-generations of larger language models. This was an incredible team effort to be a small part of, and reflects work ongoing at many labs right now. Most of the details are in the paper, but our details are a mixture of ideas that have been shown already like ServiceNow’s PipelineRL or algorithmic innovations like DAPO and Dr. GRPO. We have some new tricks too!

Some of the exciting contributions of our RL experiments are 1) what we call “active refilling” which is a way of keeping the generations from the learner nodes constantly flowing until there’s a full batch of completions with nonzero gradients (from equal advantages) – a major advantage of our asynchronous approach; and 2) cleaning, documenting, decontaminating, mixing, and proving out the large swaths of work done by the community over the last months.

The result is an excellent model that we’re very proud of. It has very strong reasoning benchmarks (AIME, GPQA, etc.) while also being stable, quirky, and fun in chat with excellent instruction following. The 32B range is largely devoid of non-Qwen competition. The scores for both of our Thinkers get within 1-2 points overall with their respective Qwen3 8/32B models – we’re proud of this!

A very strong 7B scale, Western thinking model is Nvidia’s (@NVIDIAAI) NVIDIA-Nemotron-Nano-9B-v2 hybrid model. It came out months ago and is extremely strong. I personally suspect it may be due to the hybrid architecture making subtle implementation bugs in popular libraries, but who knows.

All in, the Olmo 3 Think recipe gives us a lot of excitement for new things to try in 2026.

RL Zero

DeepSeek R1 showed us a way to new post-training recipes for frontier models, starting with RL on the base model rather than a big SFT stage (yes, I know about cold-start SFT and so on, but that’s an implementation detail). We used RL on base model as a core feedback cycle when developing the model, such as during intermediate midtraining mixing. This is viewed now as a fundamental, largely innate, capability of the base-model.

To facilitate further research on RL Zero, we released 4 datasets and series of checkpoints, showing per-domain RL Zero performance on our 7B model for data mixes focus on math, code, instruction following, and all mixed together.

In particular, we’re excited about the future of RL Zero research on Olmo 3 precisely because everything is open. Researchers can study the interaction between the reasoning traces we include at midtraining and the downstream model behavior (qualitative and quantitative).

This helps answer questions that have plagued RLVR results on Qwen models, hinting at forms of data contamination particularly on math and reasoning benchmarks (see Shao, Rulin, et al. "Spurious rewards: Rethinking training signals in rlvr." arXiv preprint arXiv:2506.10947 (2025). or Wu, Mingqi, et al. "Reasoning or memorization? unreliable results of reinforcement learning due to data contamination." arXiv preprint arXiv:2507.10532 (2025).)

What’s next

This is the biggest project we’ve ever taken on at Ai2 (@allen_ai), with 60+ authors and numerous other support staff.

In building and observing “thinking” and “instruct” models coming today, it is clear to us that there’s a very wide variety of models that fall into both of these buckets. The way we view it is that thinking and instruct characteristics are on a spectrum, as measured by the number of tokens used per evaluation task. In the future we’re excited to view this thinking budget as a trade-off, and build models that serve different use-cases based on latency/throughput needs.

As for a list of next models or things we’ll build, we can give you a list of things you’d expect from a (becoming) frontier lab: MoEs, better character training, pareto efficient instruct vs think, scale, specialized models we actually use at Ai2 internally, and all the normal things.

This is one small step towards what I see as a success for my ATOM project.

We thank you for all your support of our work at Ai2. We have a lot of work to do. We’re going to be hunting for top talent at NeurIPS to help us scale up our Olmo team in 2026.

This post in full also appears on Interconnects – the full links to the artifacts and paper are below.

Moo, moo, rawr!

🎉 Next week, I am excited to join @reflection_ai as a Member of Technical Staff to help build the open intelligence ecosystem of the Western world.

It's the most exciting opportunity to help software builders in our time, and will shape many years of AI Engineering in the medium-term before AGI. Not just about Western vs Eastern open models, but more about how AI-driven software will look like in 2030.

I spent some time articulating my thoughts about where we're going as a community and why... which became a whole blog post. Take a look, hope it interests you!
(And if it really does, we are hiring in NYC, SF, and London 😉)

alexpolozov.com/blog/reflectio…

Today we're sharing the next phase of Reflection.

We're building frontier open intelligence accessible to all.

We've assembled an extraordinary AI team, built a frontier LLM training stack, and raised $2 billion.

Why Open Intelligence Matters

Technological and scientific progress is driven by values of openness and collaboration.

The internet, Linux, and the protocols and standards that underpin modern computing are all open. This isn't a coincidence. Open software is what gets forked, customized, and embedded into systems worldwide. It's what universities teach, what startups build on, what enterprises deploy.

Open science enables others to learn from the results, be inspired by them, interrogate them, and build upon them in order to push the frontier of human knowledge and scientific advancement. AI got to where it is today through scaling ideas (e.g. self-attention, next token prediction, reinforcement learning) that were shared and published openly.

Now AI is becoming the technology layer that everything else runs on top of. The systems that accelerate scientific research, enhance education, optimize energy usage, supercharge medical diagnoses, and run supply chains will all be built on AI infrastructure.

But the frontier is currently concentrated in closed labs. If this continues, a handful of entities will control the capital, compute, and talent required to build AI, creating a runaway dynamic that locks everyone else out. There's a narrow window to change this trajectory. We need to build open models so capable that they become the obvious choice for users and developers worldwide, ensuring the foundation of intelligence remains open and accessible rather than controlled by a few.

What We've Built

Over the last year, we've been preparing for this mission.

We’ve assembled a team who have pioneered breakthroughs including PaLM, Gemini, AlphaGo, AlphaCode, AlphaProof, and contributed to ChatGPT and Character AI, among many others.

We built something once thought possible only inside the world’s top labs: a large-scale LLM and reinforcement learning platform capable of training massive Mixture-of-Experts (MoEs) models at frontier scale. We saw the effectiveness of our approach first-hand when we applied it to the critical domain of autonomous coding. With this milestone unlocked, we're now bringing these methods to general agentic reasoning.

We've raised significant capital and identified a scalable commercial model that aligns with our open intelligence strategy, ensuring we can continue building and releasing frontier models sustainably. We are now scaling up to build open models that bring together large-scale pretraining and advanced reinforcement learning from the ground up.

Safety and Responsibility

Open intelligence also changes how we think about safety. It enables the broader community to participate in safety research and discourse, rather than leaving critical decisions to a few closed labs. Transparency allows independent researchers to identify risks, develop mitigations, and hold systems accountable in ways that closed development cannot.

But openness also requires confronting the challenges of capable models being widely accessible. We're investing in evaluations to assess capabilities and risks before release, security research to protect against misuse, and responsible deployment standards. We believe the answer to AI safety is not “security through obscurity” but rigorous science conducted in the open, where the global research community can contribute to solutions rather than a handful of companies making decisions behind closed doors.

Join Us

There is a window of opportunity today to build frontier open intelligence, but it is closing and this may be the last. If this mission resonates, join us.

That’s just Grok 4