Sequential Decision Making

Multi-Turn Reinforcement Learning for LLM Agents: Why It Exists and What Makes It Hard to Build

Multi-Turn Reinforcement Learning (MTRL) trains LLM agents for sequential tasks requiring tool use and adaptation across dozens of interactions. It addresses limitations of supervised learning by enabling agents to learn from success and failure in dynamic environments, but presents significant engineering challenges.