specific method · filed under post-training
Per-dataset rollout concurrency limiting
Limits dispatcher concurrency separately by dataset to regulate each dataset's share of incoming samples.
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Evidence
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the dispatcher can limit concurrency on a per-dataset basis, which helps regulate the proportion of each dataset in the steady-state training batch, indirectly mitigating the length skew by controlling the sources of incoming samples
usedpost trainingin DeepSeek-V4.1DeepSeek
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