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Finally took time to go over Dario's essay on DeepSeek and export control and to be honest it was quite painful to read. And I say this as a great admirer of Anthropic and big user of Claude* The first half of the essay reads like a lengthy attempt to justify that closed-source

Show me your checkpoint folder and I'll let you know if I want to work with you.

AGI delayed internally.

We release a paper with a beautiful theorectial guarantee that the loss gradient in the RLHF framework is aligned with the underlying true return! It comes with a simple tweak on sampling scheme in practice; can be plug-and-play to any online setting. It's "Online Off-Policy"!

SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution arxiv.org/abs/2502.18449 by Yuxiang Wei Sida Wang and the whole team! Get started with your favorite model here github.com/facebookresear…

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Does your LLM truly comprehend the complexity of the code it generates? 🥰   Introducing our new non-saturated (for at least the coming week? 😉) benchmark:   ✨BigO(Bench)✨ - Can LLMs Generate Code with Controlled Time and Space Complexity?   Check out the details below !👇

📸 Quick snapshot of our results ! 🏅   BigO(Bench) evaluates high-level reasoning skills in coding, revealing that top-scoring models on Code Contests often struggle when required to both write and reason about their code.   Extra-Space Complexity seems particularly challenging!

New #ICLR2024 paper! The KoLMogorov Test: can CodeLMs compress data by code generation? The optimal compression for a sequence is the shortest program that generates it. Empirically, LMs struggle even on simple sequences, but can be trained to outperform current methods! 🧵1/7

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Introducing our first set of Llama 4 models! We’ve been hard at work doing a complete re-design of the Llama series. I’m so excited to share it with the world today and mark another major milestone for the Llama herd as we release the *first* open source models in the Llama 4

🚨 Your RL only improves 𝗽𝗮𝘀𝘀@𝟭, not 𝗽𝗮𝘀𝘀@𝗸? 🚨 That’s not a bug — it’s a 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 you’re optimizing. You get what you optimize for. If you want better pass@k, you need to optimize for pass@k at training time. 🧵 How?

The longer reasoning LLM thinks - the more likely to be correct, right? Apparently not. Presenting our paper: “Don’t Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning”. Link: arxiv.org/abs/2505.17813 1/n

Hello World: My team at FAIR / AI at Meta (AI Research Agent) is looking to hire contractors across software engineering and ML. If you are interested and based in the UK, please fill in the following short EoI form: docs.google.com/forms/d/e/1FAI…

Neural concrete interpretation at scale is indistinguishable from neural abstract interpretation.

The code *is* the chain-of-thought for programming. So successive iterations over the code *are* the reasoning tokens for programming.

The Soup-Salad-Sandwich category theorists are missing Omelette (everything containing eggs) saladtheory.github.io