implementation detail · filed under post-training
Composite early-stop strategy
Stops rollout when a rule fires, zeroes the outcome reward, and applies turn-specific actions and context masking.
Also called early stop strategy.
- source
- 1
- models
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- lab adopt it
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- strongest
- used
How sources treat it
One count per evidence span, weakest treatment to strongest.
used 1
Documented in
Evidence
1 span quoted from the sources, strongest treatment first.
The composite early stop strategy halts the rollout as soon as its rule fires, zeroes the outcome reward, applies separate actions to the triggering turn and earlier turns, and masks sibling contexts.
usedunclearin MiMo-V2.6 RL and OPD infrastructureXiaomi
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