Best Practices for Multi-Turn Reinforcement Learning in Amazon SageMaker AI

This post covers best practices for reliable multi-turn reinforcement learning training on Amazon SageMaker AI, emphasizing that reward quality and evaluation determine whether training produces a useful agent more than the algorithm or hyperparameters do. It covers building reproducible training environments, establishing evaluations independent of rewards, designing dense reward functions, managing context growth across turns, and monitoring training via MLflow integration.