Deploying Multi-Turn RL Infrastructure for Amazon Nova on Amazon SageMaker HyperPod

This article demonstrates how to deploy a multi-turn reinforcement learning infrastructure for Amazon Nova on SageMaker HyperPod using an event-driven pipeline that automatically trains agents on complex multi-step workflows.

• Multi-turn RL optimizes entire interaction sequences for agents executing multi-step workflows, unlike standard RLHF which optimizes single responses in isolation.
• The solution uses a two-phase deployment: Phase 1 provisions long-lived infrastructure (VPC, EKS, HyperPod, ECS, S3) via AWS CDK; Phase 2 creates ephemeral per-run resources when data is uploaded.
• Three compute layers work together: SageMaker HyperPod (P5 instances for training and generation), ECS on Fargate (reward environment workers), and Nova Forge SDK (message routing and state tracking).
• EventBridge triggers Step Functions pipeline automatically when .jsonl training data is uploaded to S3, eliminating manual orchestration.
• Infrastructure costs approximately $786–$1,180 per hour when running; cleanup script destroys resources to avoid idle charges.
• Monitoring via Step Functions console, CloudWatch logs, and SQS queue diagnostics provides visibility across all pipeline layers.

The infrastructure enables production-ready multi-turn RL training by automating provisioning, orchestration, and reward routing for agents learning complex decision-making tasks.