AWS shows how to train Qwen3-VL-8B with SkyRL on SageMaker HyperPod
AWS’s Machine Learning Blog demonstrated how to use SkyRL, an open-source reinforcement learning framework, to train Qwen3-VL-8B, a vision-language model, on Amazon SageMaker HyperPod. The setup improved the model’s maze-solving accuracy from 43.75% to 96.875% using Group Relative Policy Optimization (GRPO). The blog outlines a step-by-step workflow, including container setup, cluster launch,…
Key points
- SkyRL improved Qwen3-VL-8B’s maze-solving accuracy from 43.75% to 96.875% using GRPO on SageMaker HyperPod
- HyperPod’s cluster resiliency and FSx shared storage enable uninterrupted training and checkpoint recovery
- Deployed adapters via Ray Serve support dynamic LoRA loading for inference, compatible with OpenAI API clients
The process leverages HyperPod’s cluster resiliency—automatically replacing failed nodes and restoring training from checkpoints—to avoid lost progress. Amazon FSx for Lustre provides shared storage for model weights and evaluation outputs. The blog also details deploying the trained LoRA adapter for inference using Ray Serve, enabling dynamic loading of adapters per request. The entire workflow is designed for reproducibility, with pre-built container images and hyperparameter tuning guidance.
The story so far
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Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod
AWS Machine Learning Blog · 25 September 2026
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