Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A hands-on guide
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This article provides a hands-on guide for implementing Multi-Turn Reinforcement Learning (MTRL) on Amazon SageMaker AI to train models for agentic tasks. Unlike supervised fine-tuning, MTRL optimizes models based on the final outcome of multi-step sequences rather than individual prompts. The authors demonstrate this using a Wordle game environment hosted on Amazon Bedrock AgentCore, where the agent interacts with a policy model and receives a single reward upon completion. In their experiment, training the GPT-OSS-20B model increased its solve rate from 61% to 84% at serving settings for approximately $339 in token costs. The guide details the entire pipeline, including IAM role configuration, agent deployment, dataset preparation, and monitoring via MLflow. It also highlights critical design lessons, such as structuring rewards to prioritize outcomes over intermediate shaping and managing concurrency issues. Finally, the authors emphasize evaluating models at actual serving parameters to avoid misleading validation metrics.
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