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anakin87
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AI & ML interests

Language Models: orchestration, post-training, GRPO, synthetic data... Contributing to Haystack LLM framework ๐Ÿ—๏ธ

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reacted to theirpost with โค๏ธ about 4 hours ago
๐Ÿ“ฃ I just published a free course on Reinforcement Learning Environments for Language Models! ๐Ÿ“Œ COURSE: https://github.com/anakin87/llm-rl-environments-lil-course Over the past year, we've seen a shift in LLM Post-Training. Previously, Supervised Fine-Tuning was the most important part: making models imitate curated Question-Answer pairs. Now we also have Reinforcement Learning with Verifiable Rewards. With techniques like GRPO, models can learn through trial and error in dynamic environments. They can climb to new heights without relying on expensively prepared data. But what actually are these environments in practiceโ“ And how do you build them effectivelyโ“ Fascinated by these concepts, I spent time exploring this space through experiments, post-training Small Language Models. I've packaged everything I learned into this short course. What you'll learn ๐Ÿ”น Agents, Environments, and LLMs: how to map Reinforcement Learning concepts to the LLM domain ๐Ÿ”น How to use Verifiers (open-source library by Prime Intellect) to build RL environments as software artifacts ๐Ÿ”น Common patterns: How to build single-turn, multi-turn, and tool-use environments ๐Ÿ”น Hands-on: turn a small language model (LFM2-2.6B by LiquidAI) into a Tic Tac Toe master ๐Ÿ”ธ Build the game Environment ๐Ÿ”ธ Use it to generate synthetic data for SFT warm-up ๐Ÿ”ธ Group-based Reinforcement Learning If you're interested in building "little worlds" where LLMs can learn, this course is for you. --- ๐Ÿค—๐Ÿ•น๏ธ Play against the trained model: https://huggingface.co/spaces/anakin87/LFM2-2.6B-mr-tictactoe ๐Ÿ“š HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
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LFM2 2.6B Mr. Tic Tac Toe โŒ โญ•
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