implementation detail · filed under post-training
RL sandboxing with selective network blocking and artifact caching
Uses a sandboxing service with background processes, selective network blocking, and artifact caching for RL.
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Better sandbox infrastructure: RL moved to a new sandboxing service, enabling background processes, selective network blocking to shrink reward-hacking surface and artifact caching to avoid overloading external services.
usedsoftware implementationin Laguna S 2.1Poolside
Filed alongside
Other methods under post-training :: rollout & RL infrastructure.
Asynchronous reinforcement learningPartial rolloutAsynchronous RL frameworks for large-scale agent scaffolds and environment orchestrationToken-in-token-out (TITO)Asynchronous reinforcement learning infrastructureCo-located RL trainingData SchedulerDecoupled control plane and data planeDecoupling agent rollout into sandbox and worker containerDeficit-corrected schedulingLarge-scale asynchronous RL in synthesized tasksOne-step off-policy asynchronous reinforcement learningPredictive Rollout DispatchSample-grained garbage collectionSeamless Rollout EngineSLIMEToken-granularity persistence of rollout statesToken-level interruptionTool ManagerToolboxAdaptive Rollout ConcurrencyAdaptive Rollout SchedulingAgent LoopAgent-centric rollout execution