Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod
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This article demonstrates how to accelerate multimodal reinforcement learning training using SkyRL on Amazon SageMaker HyperPod. The authors guide readers through a workflow that trains the Qwen3-VL-8B vision-language model to navigate visual mazes using Group Relative Policy Optimization (GRPO). By leveraging HyperPod's cluster resiliency and checkpointing, the system ensures long-running jobs can recover from hardware failures without losing progress. The solution utilizes a Ray cluster topology where inference and training are colocated on shared GPUs to maximize efficiency. Training starts from a supervised fine-tuning checkpoint, improving the maze solve rate from 43.75% to over 95%. Monitoring is facilitated through pre-built Amazon Managed Grafana dashboards and the Ray Dashboard within SageMaker Studio. Finally, the trained LoRA adapters are hosted for inference using Ray Serve with dynamic loading from Amazon S3.
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