AWS Machine Learning Blog•September 25, 2026•Published September 26, 2026

Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod

SkyRL on SageMaker HyperPod slashes maze‑solving time, boosting success from 44% to 95% with resilient, multi‑node RL training.

Why Now

AWS announced a guide showing how to run the open‑source SkyRL framework on SageMaker HyperPod to train a vision‑language model for maze navigation.

What Happened

SkyRL uses Group Relative Policy Optimization (GRPO) to train a Qwen3‑VL‑8B model on a 64‑maze set, improving solve rate from 43.75% to >95%. The training runs on a HyperPod Ray cluster with 3 ml.g7e.12xlarge GPU workers and one ml.r5d.16xlarge CPU head, using FSx for Lustre for checkpointing and LoRA sync.

Why It Matters

The setup demonstrates that HyperPod’s resiliency and observability can handle long, multi‑node RL jobs, reducing downtime from hardware failures and speeding up policy convergence. It shows how large‑scale RL can be made practical for vision‑language agents.

The Limitation

The example requires a specific cluster size and instance types; results may vary with different hardware or smaller clusters.

What You Can Do

Try running SkyRL on your own SageMaker HyperPod cluster to benchmark RL training speed and success rates for your model.

Source

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Why we picked this

Core AI content: covers multimodal RL training with open-source framework and large vision-language model.