Best practices for multi-turn reinforcement learning in Amazon SageMaker AI

This article presents best practices for training multi-turn reinforcement learning agents in Amazon SageMaker AI to handle complex agentic tasks like support ticket resolution.

• Build sandboxed, reproducible training environments with read-only tools, stateful tools, or verifiable outcomes to prevent reward hacking
• Establish external evaluation independent of the reward function to measure true task success before training begins
• Design dense reward functions aligned with end-task goals, avoiding reward hacking through partial credit and multi-component scoring
• Manage multi-turn concerns: context growth, turn budgets, and separate completion from correctness metrics
• Monitor key metrics like rollout/reward, completion rate, and validation reward to detect when rewards diverge from true performance
• Follow an iterative loop: build environment, create evaluation, design reward, train, evaluate, then adjust based on trajectory analysis

SageMaker AI MTRL abstracts infrastructure complexity, allowing teams to focus on reward design and evaluation quality as the primary drivers of reliable agent training.