specific method · filed under optimization
Learning-rate elevation for training stability stress testing
Raises the learning rate in small models to reproduce large-scale instabilities.
Also called raising the learning rate.
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How sources treat it
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used 1
Documented in
Evidence
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large-scale instabilities can be reproduced in small models by raising the learning rate
usedevaluation onlyin Qwen3.8-Flash-NextQwen
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 clippingLoss masking for excessively stale tokensOff-policy sample filteringPer-token regularization for off-policy RLSafeguards against training drift and reward hackingSoft droppingTraining stability stress testingWeight clippingWeight decay coupled to learning-rate squared