Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod

This article demonstrates how to train a multimodal vision-language model using reinforcement learning on Amazon SageMaker HyperPod with SkyRL, improving maze navigation accuracy from 43.75% to over 95%.

• SageMaker HyperPod provides resilient cluster infrastructure with automatic node failure recovery and checkpoint resumption for long-running RL jobs
• SkyRL's Group Relative Policy Optimization (GRPO) trains agents over multi-turn sequences by comparing rollout trajectories within groups without requiring a separate critic model
• Colocate vLLM inference engines and FSDP policy training on the same GPUs to avoid idle compute time between rollout and training phases
• Use Amazon FSx for Lustre shared storage for checkpoint persistence, LoRA adapter synchronization, and evaluation outputs across cluster nodes
• Submit jobs remotely via sagemaker_ray:// protocol and monitor training through Ray Dashboard and Amazon Managed Grafana dashboards
• Deploy trained LoRA adapters for inference using Ray Serve with dynamic adapter loading from Amazon S3

The workflow enables efficient large-scale multimodal RL training with built-in resilience, observability, and inference hosting on Kubernetes-orchestrated GPU clusters.