Getting started with the Agent Reinforcement Trainer (ART)

This post introduces the Agent Reinforcement Trainer (ART), an open-source framework for applying reinforcement learning to LLM agents. It details the installation of ART and its dependencies (Python, pip, wandb, torchtune), and provides a concrete example of training a Qwen 2.5 model to play Tic-Tac-Toe. The example demonstrates defining a game environment, agent-environment interaction, reward assignment using an LLM-as-judge (RULER), and iterative model updates via GRPO. It highlights ART's integration with Hugging Face models and LoRA training for efficient fine-tuning.