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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,... show more

omkaar

4,344 subscribers

17,676 views • 6 months ago •via X (Twitter)

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36 Comments

omkaar6 months ago

cc for feedback: @willccb @QGallouedec @_rajanagarwal @yacinelearning @danielhanchen (learnt vllm sleep/wake from here) @karpathy (easy to port to autoresearch which is what I am doing next)

omkaar6 months ago

github:

parth6 months ago

hell yeah

omkaar6 months ago

ship squad

surya6 months ago

keep cooking omkizzy

omkaar6 months ago

me and you forever

Jibran6 months ago

@hamostaf04 this is sick

omkaar6 months ago

@hamostaf04 yessir thank you

mikael haji6 months ago

lets gooo this is fire

omkaar6 months ago

appreciate you brother

saksham6 months ago

yoooo that’s tuff

omkaar6 months ago

thank you brother

ari dutilh6 months ago

man this is awesome

omkaar6 months ago

i appreciate it man, lmk if you have ppl experimenting w LLM RL I could talk to

ari dutilh6 months ago

@novasarc01 potentially

omkaar6 months ago

@novasarc01 hi @novasarc01, would love to chat if this is interesting

Damon Crockett6 months ago

Hand wrote, nice

toki6 months ago

this is fire

omkaar6 months ago

appreciate it brother, lmk if you run any experiments on top

Madhav Singhal6 months ago

this is dope

omkaar6 months ago

thank you brother, you first put me on actual RLVR training

Chris Samra6 months ago

🔥

Fahim Ahmed6 months ago

LFG!

omkaar6 months ago

yesssir

atharva ☆6 months ago

this is incredible

omkaar6 months ago

thank you brother

Ishan6 months ago

@k7agar Nice work!

omkaar6 months ago

@k7agar thank you!

Jash Thakkar6 months ago

Nice work, will try this out if i get time to.

aarush6 months ago

so based

omkaar6 months ago

yessir down to collab on an interesting env

Krish Modi6 months ago

sick!!!

omkaar6 months ago

@therealkmodi thank you brother!

shiv6 months ago

Let's connect

adi6 months ago

holy shit

omkaar6 months ago

would love to collab if andera is looking into internal rl envs

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13,730 views • 1 month ago

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Roko 🐉

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502,696 views • 10 months ago

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Thomas Wolf

59,199 views • 2 months ago

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138,769 views • 6 months ago

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66,691 views • 1 month ago

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Sonya Huang 🐥

81,332 views • 4 months ago

A fun way to try the new Claude Fable. I asked it to train a bot using RL to navigate 3D worlds created with SpAItial AI. This is all running in the browser including RL training! Link to demo and code in comments What you can do: -> Train a bot from scratch, watch it learn in minutes, then drop it into a world it has never seen and it keeps navigating obstacles. -> The worlds are real scenes generated from a text prompt or a photo via the SpAItial AI API (you get a splat + a collision mesh). -> Mesh is made into a 2D walkability + height grid that the RL environment runs on. -> The trick to generalization: the policy only ever sees egocentric inputs. A 16-ray lidar, the goal's body frame, and its speed. No global coordinates, no map. So it learns navigation, not a memorized world.

A fun way to try the new Claude Fable. I asked it to train a bot using RL to navigate 3D worlds created with SpAItial AI. This is all running in the browser including RL training! Link to demo and code in comments What you can do: -> Train a bot from scratch, watch it learn in minutes, then drop it into a world it has never seen and it keeps navigating obstacles. -> The worlds are real scenes generated from a text prompt or a photo via the SpAItial AI API (you get a splat + a collision mesh). -> Mesh is made into a 2D walkability + height grid that the RL environment runs on. -> The trick to generalization: the policy only ever sees egocentric inputs. A 16-ray lidar, the goal's body frame, and its speed. No global coordinates, no map. So it learns navigation, not a memorized world.

RO2⚡️

12,797 views • 3 months ago

If you want to understand why RL struggles with long-horizon agent tasks, this is a good explanation. The core issue is that sparse rewards throw away most of the useful information in the trajectory. GEPA tries to learn from the trajectory itself, using reflection in text space, instead of only optimizing on the final reward. GEPA generates textual critiques of trajectories, proposes prompt edits, and then selects updates along a Pareto frontier between exploration and exploitation instead. Instead of collapsing everything into one reward number, it keeps more information about why a run failed and uses that to make legible changes. It’ll be interesting to see where this goes when people combine that kind of trajectory-level reflection with RL, using RL for optimization while preserving a much richer signal about why the agent succeeded or failed.

If you want to understand why RL struggles with long-horizon agent tasks, this is a good explanation. The core issue is that sparse rewards throw away most of the useful information in the trajectory. GEPA tries to learn from the trajectory itself, using reflection in text space, instead of only optimizing on the final reward. GEPA generates textual critiques of trajectories, proposes prompt edits, and then selects updates along a Pareto frontier between exploration and exploitation instead. Instead of collapsing everything into one reward number, it keeps more information about why a run failed and uses that to make legible changes. It’ll be interesting to see where this goes when people combine that kind of trajectory-level reflection with RL, using RL for optimization while preserving a much richer signal about why the agent succeeded or failed.

Ben Cohen

20,006 views • 4 months ago

We took a short break from robotics to build a human-level agent to play Competitive Pokémon. Partially observed. Stochastic. Long-horizon. Now mastered with Offline RL + Transformers. Our agent, trained on 475k+ human battles, hits the top 10% on Pokémon Showdown leaderboards. No search or heuristics, just sequence modeling. Today, we're open-sourcing our Metamon platform with our algorithms, data, and environments: 🌐 We are excited to see how our work accelerates research on building generally capable AI agents, and more importantly, inspires the next generation of Pokémon trainers!

We took a short break from robotics to build a human-level agent to play Competitive Pokémon. Partially observed. Stochastic. Long-horizon. Now mastered with Offline RL + Transformers. Our agent, trained on 475k+ human battles, hits the top 10% on Pokémon Showdown leaderboards. No search or heuristics, just sequence modeling. Today, we're open-sourcing our Metamon platform with our algorithms, data, and environments: 🌐 We are excited to see how our work accelerates research on building generally capable AI agents, and more importantly, inspires the next generation of Pokémon trainers!

Yuke Zhu

50,758 views • 1 year ago

Deep Research is the most fun I've had with an AI tool since NotebookLM was launched! You can't imagine how incredible this agent feels for someone like me that loves doing research. I took it for a spin here and added my thoughts: I am impressed by the reasoning capabilities of the o3 model which operated under the hood. This is RL at its best once again! Synthesizing large amounts of information from different sources is no easy feat. I can tell you all about it from my experience building and scaling RAG systems. I have so many ways I plan to use this. More on this soon.

Deep Research is the most fun I've had with an AI tool since NotebookLM was launched! You can't imagine how incredible this agent feels for someone like me that loves doing research. I took it for a spin here and added my thoughts: I am impressed by the reasoning capabilities of the o3 model which operated under the hood. This is RL at its best once again! Synthesizing large amounts of information from different sources is no easy feat. I can tell you all about it from my experience building and scaling RAG systems. I have so many ways I plan to use this. More on this soon.

elvis

26,459 views • 1 year ago

Karpathy's prediction about RL is coming true now! He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what "good" means for complex tasks. To solve this, Agents need a knowledge-guided review as a higher-dimensional feedback channel. Every major AI lab trains models with RL today (OpenAI, Anthropic, DeepSeek). And their key bottleneck has always been the reward functions. GRPO by DeepSeek worked well for math and code because the environment gave a binary signal. But for real agent tasks, someone still has to hand-code the scoring function. That takes days and breaks every time the pipeline changes. RULER (implemented in OpenPipe ART, 10k stars) addresses the exact problem Karpathy identified. The reward criteria are defined in plain English, and an LLM evaluates each trajectory against that description to provide feedback for training. I trained a Qwen3 1.4B agent that plays 2048 using GRPO with this exact workflow. In this case, the agent saw the board, picked a direction, and RULER evaluated the outcome, all from this natural language definition. You can see the full implementation on GitHub and try it yourself. Here's the ART Repo: (don't forget to star it ⭐ ) Just like RLHF replaced manual rankings and GRPO replaced the critic model, natural language rewards are replacing hand-coded scoring functions. RL reward engineering is now prompt engineering. I wrote a full walkthrough covering RL for LLM agents, from RLHF to GRPO to RULER, in the article below.

Karpathy's prediction about RL is coming true now! He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what "good" means for complex tasks. To solve this, Agents need a knowledge-guided review as a higher-dimensional feedback channel. Every major AI lab trains models with RL today (OpenAI, Anthropic, DeepSeek). And their key bottleneck has always been the reward functions. GRPO by DeepSeek worked well for math and code because the environment gave a binary signal. But for real agent tasks, someone still has to hand-code the scoring function. That takes days and breaks every time the pipeline changes. RULER (implemented in OpenPipe ART, 10k stars) addresses the exact problem Karpathy identified. The reward criteria are defined in plain English, and an LLM evaluates each trajectory against that description to provide feedback for training. I trained a Qwen3 1.4B agent that plays 2048 using GRPO with this exact workflow. In this case, the agent saw the board, picked a direction, and RULER evaluated the outcome, all from this natural language definition. You can see the full implementation on GitHub and try it yourself. Here's the ART Repo: (don't forget to star it ⭐ ) Just like RLHF replaced manual rankings and GRPO replaced the critic model, natural language rewards are replacing hand-coded scoring functions. RL reward engineering is now prompt engineering. I wrote a full walkthrough covering RL for LLM agents, from RLHF to GRPO to RULER, in the article below.

Avi Chawla

350,798 views • 4 months ago

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.

elvis

141,023 views • 1 year ago

So I’ve officially changed my username here to just be me. I’ve also changed the links to my Self Love Circle links. If you are looking for “Dee Siren” content then you will need to go find those links on my Dee Siren - SirenXXXStudios Legacy Account here and on Instagram or look it up. Over the last 5 years, I have finally learned that everything in my life starts with me and if I am going to be able to help others through this awakening process, I had to learn to love myself. If you want to go on this journey with me please sign up for my Free option Patreon Or join as a member. I will also be offering membership content if you subscribe here on X. I am excited about this journey and hope you will stay on this path to discovering your own Dharma with me.

So I’ve officially changed my username here to just be me. I’ve also changed the links to my Self Love Circle links. If you are looking for “Dee Siren” content then you will need to go find those links on my Dee Siren - SirenXXXStudios Legacy Account here and on Instagram or look it up. Over the last 5 years, I have finally learned that everything in my life starts with me and if I am going to be able to help others through this awakening process, I had to learn to love myself. If you want to go on this journey with me please sign up for my Free option Patreon Or join as a member. I will also be offering membership content if you subscribe here on X. I am excited about this journey and hope you will stay on this path to discovering your own Dharma with me.

Cylla Kavanaugh

31,989 views • 7 months ago

This Indian Developer made 41.7 Lakh in roughly 2 months. He builds RL environments for AI labs. Here’s what that actually means. You don’t write answers for the AI. You set its exam. Take a real GitHub repo with a real bug. Pin it inside Docker so it runs identically every time. Write the task. Write a grader that returns an automatic pass or fail. Write your own gold solution to prove it’s solvable. Then make sure the model can’t cheat its way to a pass. The labs have run out of internet data. This is the next lever. Anthropic reportedly discussed spending over $1B on this in a single year. Where to start: Prime Intellect’s Environments Hub is free to enter and has 2,500+ open environments. Ship one as a public PR. Then apply to Mercor, Surge or Handshake with that PR as proof. Week 1 he made ₹6,880. Start. Comment “RL” for the full breakdown and link to Whatsapp Channel in the bio! #ai #rl #hiring #google

This Indian Developer made 41.7 Lakh in roughly 2 months. He builds RL environments for AI labs. Here’s what that actually means. You don’t write answers for the AI. You set its exam. Take a real GitHub repo with a real bug. Pin it inside Docker so it runs identically every time. Write the task. Write a grader that returns an automatic pass or fail. Write your own gold solution to prove it’s solvable. Then make sure the model can’t cheat its way to a pass. The labs have run out of internet data. This is the next lever. Anthropic reportedly discussed spending over $1B on this in a single year. Where to start: Prime Intellect’s Environments Hub is free to enter and has 2,500+ open environments. Ship one as a public PR. Then apply to Mercor, Surge or Handshake with that PR as proof. Week 1 he made ₹6,880. Start. Comment “RL” for the full breakdown and link to Whatsapp Channel in the bio! #ai #rl #hiring #google

Arsh Goyal

38,764 views • 1 month ago