Stefano Fiorucci

This talk explores engineering reinforcement learning (RL) environments for language models, enabling them to learn through interaction, exploration, and feedback. It highlights how these environments serve as crucial training grounds for LLM agents, allowing them to develop skills in tool use, code execution, and complex task solving. The presentation introduces Verifiers, an open-source library for building such environments, and demonstrates their application through an experiment transforming a basic tic-tac-toe playing model into a master.