implementation detail · filed under optimization
Soft dropping
A method applied to overflow tokens in a mixture-of-experts setting; the evidence does not further specify its mechanism.
- source
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- model
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- lab adopt it
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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.
soft dropping for overflow tokens
usedmodel architecturein Kimi K3Moonshot AI
Filed alongside
Other methods under optimization :: training stability.
Weight paddingActivation or logit clippingAnti-hallucination trainingBehavioral regularizationCross-replica model-weight hash consistency checksElevated constant-learning-rate training stability stress testExponential moving average of checkpointsGradient clippingLearning-rate elevation for training stability stress testingLoss masking for excessively stale tokensOff-policy sample filteringPer-token regularization for off-policy RLSafeguards against training drift and reward hackingTraining stability stress testingWeight clippingWeight decay coupled to learning-rate squared