Best Practices for Multi-Turn RL

The blog post explores the evolution of AI agents from simple single-turn interactions to complex multi-turn, tool-heavy tasks, emphasizing the limitations of Supervised Fine-Tuning (SFT) and the advantages of Reinforcement Learning (RL) in these scenarios. It outlines the anatomy of a multi-turn RL system, highlighting the need for careful reward design and the challenges of training agents to effectively plan, call tools, and recover from mistakes. The post stresses the importance of trajectory-level rewards, environment stability, and using a strong base model to ensure success. It provides practical insights and strategies, including environment engineering, exploration encouragement, and training close to production, to make multi-turn RL systems work effectively. Through a case study of a deep research agent, the post demonstrates how RL can transform a weaker language model into a specialized agent that surpasses frontier models in specific workflows, underscoring the significance of RL in advancing AI capabilities for complex decision-making tasks.