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๐ฑ A little course on Reinforcement Learning Environments for evaluating and training Language Models

A little course on Reinforcement Learning Environments for evaluating and training Language Models.
Unlike classic fine-tuning, RL environments let models explore and improve beyond what curated datasets can teach.
In this course, we'll build a Tic Tac Toe environment and use it to transform a Small Language Model
(LiquidAI/LFM2-2.6B) into a master player that beats gpt-5-mini.
โก๏ธ Start here: Chapter 1 - Agents, Environments, and LLMs
๐ฅ Video walkthrough @ AI Engineer
๐ค๐น๏ธ Play against Mr. Tic Tac Toe
โก๏ธ Start here: Chapter 1 - Agents, Environments, and LLMs
This course is not affiliated with any of the following projects:
| Project | Description |
|---|---|
![]() Verifiers | An open-source library by Prime Intellect for building RL environments as software artifacts |
![]() Liquid AI models | Small, fast Language Models based on a novel architecture |
![]() vLLM | High-throughput and memory-efficient serving engine for LLMs |
Stefano Fiorucci/anakin87
I built this course from hands-on experimentation. If you spot any errors, please open a GitHub issue.
Feel free to follow me on my social profiles: GitHub, LinkedIn, X, Hugging Face.
โ ๏ธ Experimentelle Skill-Sammlung fรผr deutsches Recht (Arbeits-, Gesellschafts-, Insolvenz-, Datenschutz-, Prozessrecht u
Manage multiple Claude Code agents from TUI or Web with tmux and git worktrees
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