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

Amazon Web Services (AWS) has published new best practices for implementing multi-turn reinforcement learning (RL) on its Amazon SageMaker AI platform. These guidelines are aimed at training AI agents for tasks requiring sequential, dependent actions, such as resolving support tickets or moderating content.

Multi-turn agents operate by reading instructions, making tool calls, interpreting results, deciding subsequent actions, and recovering from errors before committing to a final response. AWS highlights that this operational flexibility makes agentic RL training challenging, as more potential actions can lead to satisfying the reward without completing the task. The training environment itself can also subtly corrupt the training signal.

The published practices detail how to build a reliable training environment, set up external evaluations, design rewards aligned with the end task, and manage agent actions over multiple turns. They also offer advice on monitoring metrics for training iteration. Examples are drawn from the SOP-Bench dataset, an Amazon Science benchmark evaluating agents' ability to resolve tasks based on complex Standard Operating Procedures.

Amazon SageMaker AI MTRL provides the training loop for agentic tasks, allowing agents to run on various AWS services like Amazon Bedrock AgentCore, Amazon EKS, Amazon EC2, and AWS Fargate. The platform offers a modular agent-environment interface for low-code integration and full algorithmic control, including custom rewards and multi-turn conversation shapes. The objective is to enhance agent reliability and efficiency in complex tasks.