Reinforcement learning (RL) post-training is becoming a standard step in building capable language model agents. Models learn to reason and act across sequences of steps by generating trajectories, receiving rewards, and updating their policy based on outcomes. Running this at scale, across multiple nodes with hundreds of GPU-hours of rollouts per training run, requires persistent cluster infrastructure. That infrastructure needs to sustain long jobs, recover from hardware failures without losing progress, and provide visibility into training dynamics as they unfold.

Amazon SageMaker HyperPod provides this infrastructure for large-scale machine learning (ML) workloads on Amazon Elastic Kubernetes Service (Amazon EKS). Through its cluster resiliency features, it continuously monitors node health and automatically replaces faulty nodes, so a hardware failure does not take the cluster down with it. Paired with checkpointing, a training job can pick up from its last saved step instead of restarting from scratch. This matters for long multi-node RL runs, where a single hardware failure would otherwise cost hours of rollout progress. Combined with the Ray capabilities on HyperPod, you can create Ray clusters from SageMaker Studio, submit jobs remotely using secure connections, and monitor training through pre-built Amazon Managed Grafana dashboards that the HyperPod Observability EKS add-on provisions for you.

In this post, we show how to use these capabilities to run SkyRL , an open-source RL framework, to train a Qwen3-VL-8B vision-language model to navigate visual mazes using Group Relative Policy Optimization (GRPO) on SageMaker HyperPod. 75% to more than 95% on a fixed 64-maze evaluation set.

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