implementation detail · filed under post-training
Staleness-aware truncated importance sampling
Uses truncated importance sampling that accounts for staleness when training with partial rollouts.
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How sources treat it
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used 1
Documented in
Evidence
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By employing staleness-aware truncated importance sampling for partial rollout, we significantly accelerate RL training without sacrificing model quality.
usedtraining objectivein MiMo-V2-FlashXiaomi
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