Integration of SkyRL and Amazon SageMaker HyperPod

SkyRL is an open-source framework that optimizes the training of reinforcement learning (RL). This framework collaborates with Amazon SageMaker HyperPod to accelerate the training of multimodal RL. SageMaker HyperPod operates on Amazon Elastic Kubernetes Service (EKS) and provides a fault-tolerant cluster infrastructure that supports long-running jobs. (Source: Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod)

Post-Training of Qwen3-VL-8B using GRPO

Qwen3-VL-8B is a vision-language model that can be post-trained using Group Relative Policy Optimization (GRPO). This model starts from the VisGym SFT checkpoint and learns to navigate visual mazes. GRPO is an algorithm that compares multiple policies to determine the optimal action, improving training efficiency. (Source: Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod)

Details of the Training Workflow

This workflow includes the following steps:

  1. Building Container Images: Builds a custom container that includes SkyRL and its dependencies.
  2. Launching Ray Clusters: Generates a Ray cluster from SageMaker Studio and sends jobs via remote connection.
  3. Running and Monitoring Jobs: Uses Amazon Managed Grafana dashboards to monitor the progress of training.
  4. Hosting LoRA Adapters: Hosts trained LoRA adapters for inference. (Source: Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod)

Performance and Recovery Features

SageMaker HyperPod has cluster fault tolerance, continuously monitoring node health and automatically replacing failed nodes to prevent training job interruptions. Additionally, the checkpoint feature allows resuming from the last saved step, avoiding progress loss due to hardware failures. (Source: Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod)

Summary

  • Utilizes SkyRL on Amazon SageMaker HyperPod to enable post-training of Qwen3-VL-8B’s vision-language model using GRPO.
  • Executes a workflow that includes building container images to hosting LoRA adapters.
  • SageMaker HyperPod’s fault tolerance and checkpoint features enable stable execution of long multi-node training sessions.