Bringing gVisor sandboxes to distributed Ray clusters

The reinforcement learning (RL) ecosystem is rapidly adopting Ray as the unified compute runtime for complex post-training workflows. Across Google Cloud, we see customers using Ray for workloads ranging from multimodal data pipelines to frontier RL. But as agentic and reasoning models evolve, a critical bottleneck has emerged: orchestrating secure, isolated sandboxes at scale to safely execute dynamic rollouts, code generation, and multi-turn tool interactions.