RL Environments for Language Models: I built a hands-on free course

🌱 Course: https://github.com/anakin87/llm-rl-environments-lil-course |
🎥 Video: https://www.youtube.com/watch?v=71V3fTaUp2Q

I've been deep into RL for LLMs lately.

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 reach new heights without expensive 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.

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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 that beats GPT-5-mini

• 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.

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🕹️ Play against the trained model: https://huggingface.co/spaces/anakin87/LFM2-2.6B-mr-tictactoe

🤗 HF collection with datasets and models: https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe