Amazon has published a guide on best practices for multi-turn reinforcement learning (RL) using Amazon SageMaker AI. This service provides a training loop for agentic tasks, allowing developers to manage multi-step processes like support ticket resolution or content moderation. Agents trained via this service interact with tools across multiple turns, requiring careful environment design to avoid issues like reward hacking, where the model satisfies criteria without performing the task. The service supports various infrastructure options including Amazon Bedrock AgentCore, Amazon EKS, and AWS Fargate, providing a modular interface for defining rewards and tool loops.

Effective multi-turn RL training relies on building isolated, reproducible simulated environments. Because agents explore via trial and error—potentially performing unintended actions like deleting records or issuing refunds if pointed at live systems—developers should utilize sandboxed environments. Common patterns for these simulations include using read-only tools with recorded responses, stateful tools that clear resources after each episode, or verifiable execution environments like Docker containers for code, SQL, or math tasks. Consistent schemas and business logic must match production standards to ensure that the learned behavior transfers correctly.

Before initiating training, developers must establish an external, independent evaluation to monitor success beyond the training reward. A small piece of code running on a fixed test split provides a ground-truth metric, such as an exact-match score used in the SOP-Bench dataset, which evaluates agent performance across 12 business domains. This external check prevents the model from optimizing for metrics that do not translate to production success. The service includes a native algorithm library covering techniques such as Proximal Policy Optimization (PPO), Clipped Importance Sampling Policy Optimization (CISPO), and group-based advantage estimators like GRPO.

Reward function design is central to model performance. While sparse rewards may lead to training stalls if the base model lacks a foothold, dense rewards can guide the agent by providing feedback on partial progress. Developers are advised to ensure that training rewards and evaluation metrics align, verifying these signals on real outputs before scaling. If a base model produces zero successful trajectories, training will likely fail; therefore, baseline evaluation is necessary to confirm the model can achieve success on a held-out prompt set before reward-based optimization begins.

Amazon has published a guide on best practices for multi-turn reinforcement learning (RL) using Amazon SageMaker AI. This service provides a training loop for agentic tasks, allowing developers to manage multi-step processes like support ticket resolution or content moderation. Agents trained via this service interact with tools across multiple turns, requiring careful environment design to avoid issues like reward hacking, where the model satisfies criteria without performing the task. The service supports various infrastructure options including Amazon Bedrock AgentCore, Amazon EKS, and AWS Fargate, providing a modular interface for defining rewards and tool loops.

Effective multi-turn RL training relies on building isolated, reproducible simulated environments. Because agents explore via trial and error—potentially performing unintended actions like deleting records or issuing refunds if pointed at live systems—developers should utilize sandboxed environments. Common patterns for these simulations include using read-only tools with recorded responses, stateful tools that clear resources after each episode, or verifiable execution environments like Docker containers for code, SQL, or math tasks. Consistent schemas and business logic must match production standards to ensure that the learned behavior transfers correctly.

Before initiating training, developers must establish an external, independent evaluation to monitor success beyond the training reward. A small piece of code running on a fixed test split provides a ground-truth metric, such as an exact-match score used in the SOP-Bench dataset, which evaluates agent performance across 12 business domains. This external check prevents the model from optimizing for metrics that do not translate to production success. The service includes a native algorithm library covering techniques such as Proximal Policy Optimization (PPO), Clipped Importance Sampling Policy Optimization (CISPO), and group-based advantage estimators like GRPO.

Reward function design is central to model performance. While sparse rewards may lead to training stalls if the base model lacks a foothold, dense rewards can guide the agent by providing feedback on partial progress. Developers are advised to ensure that training rewards and evaluation metrics align, verifying these signals on real outputs before scaling. If a base model produces zero successful trajectories, training will likely fail; therefore, baseline evaluation is necessary to confirm the model can achieve success on a held-out prompt set before reward-based optimization begins.