SkyRL Trains Vision Models to 95% Maze Accuracy

Amazon SageMaker HyperPod now supports SkyRL, an open-source reinforcement learning framework, enabling large-scale multimodal RL training on Kubernetes-managed GPU clusters. The infrastructure handles node health monitoring, automatic failure recovery, and checkpoint-based job resumption, critical for long multi-node training runs. Using Group Relative Policy Optimization, a Qwen3-VL-8B vision-language model trained on HyperPod improved maze-solving accuracy from 43.75% to over 95% on a 64-maze benchmark. The setup requires at least three GPU worker nodes, FSx for Lustre shared storage, and SageMaker Studio for remote job submission and Grafana-based monitoring.