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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 🏗️

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repliedto their post about 5 hours ago
📣 I just published a free course on Reinforcement Learning Environments for Language Models! 📌 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 practice❓ And how do you build them effectively❓ 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 🔹 Agents, Environments, and LLMs: how to map Reinforcement Learning concepts to the LLM domain 🔹 How to use Verifiers (open-source library by Prime Intellect) to build RL environments as software artifacts 🔹 Common patterns: How to build single-turn, multi-turn, and tool-use environments 🔹 Hands-on: turn a small language model (LFM2-2.6B by LiquidAI) into a Tic Tac Toe master 🔸 Build the game Environment 🔸 Use it to generate synthetic data for SFT warm-up 🔸 Group-based Reinforcement Learning If you're interested in building "little worlds" where LLMs can learn, this course is for you. --- 🤗🕹️ Play against the trained model: https://huggingface.co/spaces/anakin87/LFM2-2.6B-mr-tictactoe 📚 HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
reacted to theirpost with 😎 about 5 hours ago
🌀 Let LLMs wander - Engineering RL Environments Reinforcement Learning Environments are little worlds where models can act, get rewards, and learn. I've been exploring how to design them, figuring out what works and what doesn't. If you want to learn how to build them, I recorded a practical intro video. You'll also see how to turn Liquid AI LFM2-2.6B into a Tic-tac-toe master 🙂 🎥 Engineering RL Environments video: https://www.youtube.com/watch?v=71V3fTaUp2Q --- 🌱 LLM RL Environments Lil Course: https://github.com/anakin87/llm-rl-environments-lil-course 🤗🕹️ Play against the trained model: https://huggingface.co/spaces/anakin87/LFM2-2.6B-mr-tictactoe 📚 HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
posted an update about 5 hours ago
🌀 Let LLMs wander - Engineering RL Environments Reinforcement Learning Environments are little worlds where models can act, get rewards, and learn. I've been exploring how to design them, figuring out what works and what doesn't. If you want to learn how to build them, I recorded a practical intro video. You'll also see how to turn Liquid AI LFM2-2.6B into a Tic-tac-toe master 🙂 🎥 Engineering RL Environments video: https://www.youtube.com/watch?v=71V3fTaUp2Q --- 🌱 LLM RL Environments Lil Course: https://github.com/anakin87/llm-rl-environments-lil-course 🤗🕹️ Play against the trained model: https://huggingface.co/spaces/anakin87/LFM2-2.6B-mr-tictactoe 📚 HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
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