Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod
Learn how to run SkyRL, an open-source reinforcement learning framework, on Amazon SageMaker HyperPod to post-train a Qwen3-VL-8B vision-language model with GRPO. This walkthrough covers building the container image, launching a Ray cluster from SageMaker Studio, submitting and monitoring the job, and hosting the trained LoRA adapter for inference.
Reinforcement learning (RL) modelling techniques, such as Group Relative Policy Optimization (GRPO), enable language model agents to learn through sequential decision making. Amazon SageMaker HyperPod provides the necessary infrastructure for large-scale machine learning (ML) training workloads. This includes cluster resiliency, checkpointing, and Ray capabilities.
In this walkthrough, a vision-language model, Qwen3-VL-8B, is trained on a HyperPod Ray cluster using SkyRL to navigate visual mazes with GRPO. The training process involves running the model several times under current policies, comparing these runs against one another, and reinforcing the successful ones while penalizing the less successful ones.
This approach allows GRPO to work without a separate critic or value model. The training topology uses three GPU worker nodes and a CPU head node. The model, Qwen3-VL-8B, is sharded across the six GPUs using Fully Sharded Data Parallel (FSDP). The rollout engines are six colocated vLLM instances, each on a separate GPU. The inference and training are colocated on the same GPUs, with updates from gradient updates synced to the inference engines through shared storage.
Amazon FSx for Lustre is used as shared storage, providing the necessary infrastructure for checkpoint storage, LoRA adapter synchronization, and evaluation output.
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