DataScience-UIBK/Reason-mxbai-colbert-v0-32m
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David Golchinfar PRO
DavidGF
AI & ML interests
finetune llms, improve german language understanding and generated text of llms
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liked a model about 19 hours ago
DataScience-UIBK/Reason-mxbai-colbert-v0-32m liked a Space 1 day ago
anakin87/LFM2-2.6B-mr-tictactoeOrganizations
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A small model that struggled against a random opponent now beats GPT-5-mini at tic-tac-toe
I took LiquidAI/LFM2-2.6B and trained it through play.
🧑🍳 Here's how:
1️⃣ Build a solid RL env with Verifiers (Prime Intellect)
2️⃣ Generate synthetic data: <200 games sampled from GPT-5-mini playing in the env
3️⃣ SFT warm-up to teach format
4️⃣ Group-based RL (CISPO) against opponents making 20-70% random moves
5️⃣ RL again with stronger opponents (0-25% random moves) + 1.25 temperature to push exploration and shake off suboptimal strategies
Done! Beats GPT-5-mini 🏆
---
🎮 Play against the model: anakin87/LFM2-2.6B-mr-tictactoe
🤗 Model: anakin87/LFM2-2.6B-mr-tictactoe
📚 Walkthrough/course: https://github.com/anakin87/llm-rl-environments-lil-course
🤗 Dataset and checkpoints: https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
I took LiquidAI/LFM2-2.6B and trained it through play.
🧑🍳 Here's how:
1️⃣ Build a solid RL env with Verifiers (Prime Intellect)
2️⃣ Generate synthetic data: <200 games sampled from GPT-5-mini playing in the env
3️⃣ SFT warm-up to teach format
4️⃣ Group-based RL (CISPO) against opponents making 20-70% random moves
5️⃣ RL again with stronger opponents (0-25% random moves) + 1.25 temperature to push exploration and shake off suboptimal strategies
Done! Beats GPT-5-mini 🏆
---
🎮 Play against the model: anakin87/LFM2-2.6B-mr-tictactoe
🤗 Model: anakin87/LFM2-2.6B-mr-tictactoe
📚 Walkthrough/course: https://github.com/anakin87/llm-rl-environments-lil-course
🤗 Dataset and checkpoints: https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
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🌀 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: anakin87/LFM2-2.6B-mr-tictactoe
📚 HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
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: 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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