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Hiring RL Engineer! Started off as a curious project at Lossfunk to push the boundaries of LLMs in social reasoning - we are now building RL environments, data, and benchmarks to simulate more real-world scenarios. If you want to train SoTA RL models over multi-GPUs (H200s/B200s) to unlock next... show more

Satpal Singh Rathore

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46,010 Aufrufe • vor 1 Jahr •via X (Twitter)

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I like to think of evals as something active, not passive -- it's a North Star that steers LLMs toward higher intelligence. Evals should drive your RL/SFT/post-training decisions. Internal evals at frontier labs make a huge difference -- and you can see it in how models behave differently (GPT seems better at one-shot tasks, Claude at multi-turn). If you want to learn more about building evals that actually improve your model in post-training, check out our AMD x DeeplearningAI course "Fine-tuning & RL for LLMs: Intro to Post-training" (content is free):

I like to think of evals as something active, not passive -- it's a North Star that steers LLMs toward higher intelligence. Evals should drive your RL/SFT/post-training decisions. Internal evals at frontier labs make a huge difference -- and you can see it in how models behave differently (GPT seems better at one-shot tasks, Claude at multi-turn). If you want to learn more about building evals that actually improve your model in post-training, check out our AMD x DeeplearningAI course "Fine-tuning & RL for LLMs: Intro to Post-training" (content is free):

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"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

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Prime Intellect engineers explained how they train reasoning models over the open internet in 30 minutes - better than $3000 distributed training courses. split policy and rollouts across nodes -> run agents in parallel envs -> verify with LLM judges -> gradient-sync over the internet -> train Llama, Qwen, Gemma at cluster scale on rented GPUs. That loop is why open reasoning models are catching closed labs without owning a data center. Prime-RL + verifiers + distributed rollouts + LLM judges + multi-cloud GPUs - that's the stack. Watch and save it, then launch your first distributed RL run this week.

h100envy

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Y Combinator

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I hand-wrote a 500-LoC RL stack to make hacking on RL research much easier. Most RL stacks are either massive and unhackable, or duct-taped research scripts. I am open-sourcing Mithrl, a modular RLVR stack. Next items on my checklist: adding more complex environment examples, supporting multi-gpu + async RL, and QoL fixes. I might scrap external runtime dependencies (Huggingface PEFT + vLLM) and write purpose-built, simpler versions from scratch if I feel the need. If you want to experiment with RL and are looking to own sovereign tools, I’d love to get on call, understand your requirements and help integrate for free.

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Interactive Reasoning Benchmarks are the next step in frontier evaluations Hear Greg Kamradt share why measuring human-like intelligence requires multi-turn environments Including a sneak peak of ARC-AGI-3 Want to help us build interactive evaluations? We're hiring

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ARC Prize

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Applied Compute

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Lecture 10 of my course! Nominally on regularization in RL, so I discuss the evolving role of the KL penalty in RL, but also a set of nice RL papers that explain what RL helps models generalize better than SFT -- with theory supporting it. When going through these, it's so interesting how seasonal problems in ML are. Lots of problems from controlling reward models overopt will rhyme as we try to control rubrics for agents. 00:00 Intro & the role of regularization 02:50 The KL penalty in RL 10:53 RL as a reverse KL loss 21:09 Why RL generalizes better than SFT 25:15 Other regularization tools Just a few videos left as I get to the end of the course. Thanks all, and keep sending questions. Spread the word if you have a second.

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Take Part in a Groundbreaking Experiment in AI-Based Gaming! Introducing: AI Arena RL @aiarena_ RL is a groundbreaking experiment where your gameplay shapes AI agents, pioneering the future of AI-powered esports. Powered by NRN Agents Reinforcement Learning (RL), this campaign uses player-contributed gameplay data to create superhuman RL agents that compete in AI vs. AI esports exhibitions. The Ronin community has exclusive access to contribute data to these agents. Phase 1 - How It Works: ✅ Sign in to with your Ronin Wallet. ✅ Choose your AIcon – Select which RL agent learns from your gameplay: jAIhoz.ron AgentYP Agent J3ff @AgentSploots ✅ Play AI Arena Classic – A Smash Bros-style platform fighter. ✅ Build your Contribution Score – Better gameplay means higher scores. ✅ Earn $NRN Rewards – Share in the 500,000 NRN prize pool: 🔹 80% distributed to Players (data contributors) 🔹 20% awarded to AIcons based on leaderboard rank at the end of the campaign Phase 1 of the campaign kicks off now and will last for 48 hrs. For complete information about the campaign please visit: History Is Being Made Right Now—Don't Miss Your Chance to Be Part of It. Are you ready to redefine the future of AI gaming?

NRN Agents

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