The reinforcement learning (RL) ecosystem is rapidly adopting Ray as the unified compute runtime for complex post-training workflows. 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.
The reinforcement learning (RL) ecosystem is rapidly adopting Ray as the unified compute runtime for complex post-training workflows.
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...
Sandboxes as Ray Primitives Ray has become a common runtime for orchestrating post-training workloads.
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The reinforcement learning (RL) ecosystem is rapidly adopting Ray as the unified compute runtime for complex post-training workflows. 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. Sandboxes as Ray Primitives Ray has become a common runtime for orchestrating post-training workloads.
Today, in partnership with Anyscale, we are excited to introduce an experimental library for Ray that leverages agentic AI technologies being developed at Google to bring native, high-performance sandboxing directly into distributed Ray clusters.
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