Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A hands-on guide

This AWS post walks through an end-to-end Multi-Turn Reinforcement Learning pipeline on Amazon SageMaker AI, using a Wordle agent hosted on Amazon Bedrock AgentCore that reports one outcome reward per episode for GRPO training of a LoRA adapter. In the example run, gpt-oss-20b improved from 61% to 84% solve rate on held-out words at default reasoning effort and from 15% to 69% at low effort, for about $339 in training tokens. It also covers IAM roles, dataset preparation, MLflow monitoring, serving-setting evaluation, cleanup, and reward-design mistakes and training failures encountered.