specific method · filed under post-training
Sample-level dispatch
Dispatches the next prompt once enough newly completed samples accumulate to meet its assigned GRPO group size, regardless of their originating groups.
- source
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- model
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- lab adopt it
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- strongest
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How sources treat it
One count per evidence span, weakest treatment to strongest.
default 1
Documented in
Evidence
1 span quoted from the sources, strongest treatment first.
Our final approach is sample-level dispatch: once the number of newly completed samples reaches the GRPO group size assigned to the next prompt, we dispatch that prompt regardless of which groups produced those completions.
defaultpost 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