Accelerate agentic RL with GKE Agent Sandbox
When scaling up agentic reinforcement learning (RL) and evaluation across massive parallel rollouts, frontier AI labs inevitably hit a bottleneck: Expensive GPU clusters sit idle, waiting minutes for CPU sandbox cold-starts, plus thousands of multi-gigabyte SWE-bench-style image pulls and scheduling backlogs. It’s a sandbox infrastructure problem that silently slows down your research and burns your training budget.
To solve this fundamental infrastructure bottleneck, today we are introducingGKE Agent Sandboxoptimized for RL along with the Agent Sandbox RL orchestration SDK, plus native integrations for popular RL gyms and harnesses, now generally available.
As the operating system for modern AI, Kubernetes has evolved to power massive GPU/TPU training clusters and distributed inference. Now Kubernetes is expanding to drive the next AI compute frontier: agents. But unlike static workloads, agentic workloads evolve rapidly, so infrastructure must evolve just as fast. Rather than guessing at what RL researchers needed, we...
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