My RL team at @amazon NYC is looking for summer 2026 PhD interns! We apply RL to Amazon's supply chain and do publishable, open-ended research in meta learning, multi-agent RL, constrained RL, exploration, and LLMs+RL. Interested? Email your CV to akhilbg@amazon.com by Nov 30th!

Introducing Grok 4.1, a frontier model that sets a new standard for conversational intelligence, emotional understanding, and real-world helpfulness. Grok 4.1 is available for free on https://t.co/AnXpIEOPEb, https://t.co/53pltyq3a4 and our mobile apps. https://t.co/Cdmv5CqSrb

Very excited that our AlphaProof paper is finally out! It's the final thing I worked on at DeepMind, very satisfying to be able to share the full details now - very fun project and awesome team! https://t.co/OuWDemzAt4

I'm looking for a student researcher to join me at @GoogleDeepMind Montreal to work on getting the best generative AI into the hands of the best creative people. Your work will push models past their current frontiers and directly benefit creative engagements. Are you

Come work with me on OLMo! https://t.co/YiWhZtrcvz

After 7 years at FAIR, I've been affected by the recent AI layoffs. If you are interested in robotics learning, let's chat :)

Recommendation system with AI will be a complete game changer. This is nothing like traditional algorithms that recommend based on heuristics. @grok will read every post, understand you even better than yourself, and recommend contents you like. If your content is genuinely

Wish to build scaling laws for RL but not sure how to scale? Or what scales? Or would RL even scale predictably? We introduce: The Art of Scaling Reinforcement Learning Compute for LLMs

🤔Can we train RL on LLMs with extremely stale data? 🚀Our latest study says YES! Stale data can be as informative as on-policy data, unlocking more scalable, efficient asynchronous RL for LLMs. We introduce M2PO, an off-policy RL algorithm that keeps training stable and

To the degree that people are seeing improvements in their feed, it is not due to the actions of specific individuals changing heuristics, but rather increasing use of Grok and other AI tools. We will post the updated algorithm, including model weights, later this week. You

More on LLMs, RL, and the bitter lesson, on the Derby Mill podcast.

📣 Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels 📣 RL for LLMs faces a critical data bottleneck: existing RL datasets are <10B tokens while pretraining uses >1T tokens. Our Webscale-RL pipeline solves this by automatically converting pretraining

(1/n) Neural networks trained with Adam record a map of their learning journey—the optimizer's second moments (exp_avg_sq). These approximate the Hessian (curvature) at training's end. We used this insight to uncover why model merging works & built stronger merging methods 🧵

At @ChandarLab, we are happy to announce the third edition of our assistance program to provide feedback for members of communities underrepresented in AI who want to apply to high-profile graduate programs. Want feedback? Details: https://t.co/0K6AA4gV0U. Deadline: Nov 01! cc:

when you give up on this nebulous idea and illusion of prestige, you will finally find peace and freedom. submit to TMLR and JMLR.

We just released a model that’s both fast and smart, beating all models of the same size by a huge margin. It’s available for free. Give it a try!

For anyone worried their LLM might be making stuff up, we made a budget‐friendly truth serum (semantic entropy + Bayesian). See for yourself: https://t.co/gq8oFP5Eqr Paper:

https://t.co/cGnMlQSoJn

We're hiring Research Scientists in Tech Research @RiotGames! If you're passionate about shaping the future of games with AI and have research experience in RL, IL, or generative methods, we'd love to hear from you. DM me if interested.

@AndyKhris2 we're building a community. we'd love for you to join. https://t.co/8NwezP5Mif