Best practices for multi-turn reinforcement learning in Amazon SageMaker AI | Amazon Web Services
Training a multi-turn agent in Amazon SageMaker AI to resolve support tickets or moderate content means handling a sequence of dependent steps, not a single response. These agents read instructions, make tool calls, read the results, decide the next action, and recover from a mistake before committing to an answer. That flexibility is also what makes agentic reinforcement learning (RL) challenging. More ways to act mean more ways to satisfy the reward without doing the task, and the environment the agent trains against can quietly corrupt the training signal.
In this post, we share best practices for reliable multi-turn RL training. We cover how to build a training environment you can trust, set up an external evaluation, design a reward aligned with the end task, manage what changes once the agent runs for multiple turns, and monitor the metrics that tell you when to iterate. We draw our examples from...
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