Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod

Extracto de la fuente

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…