A community-driven registry for Claude, Cursor, Windsurf, Cline & more. Not affiliated with Anthropic.
Are you the author? Sign in to claim
🌱 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.
➡️ Start here: Chapter 1 - Agents, Environments, and LLMs
🎥 Video walkthrough @ AI Engineer
🤗🕹️ Play against Mr. Tic Tac Toe
➡️ 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.
Pocket Flow: Codebase to Tutorial
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
干净、强大、属于你的 AI Agent 平台 --AI agents, without the clutter.
💻 A curated list of papers and resources for multi-modal Graphical User Interface (GUI) agents.