A one-parameter scaling law models excess loss from data repetition as an additive overfitting penalty, recommending model capacity increases over excessive repetition and showing that strong weight decay reduces the penalty coefficient by ~70%.
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Prescriptive Scaling Laws for Data Constrained Training
A one-parameter scaling law models excess loss from data repetition as an additive overfitting penalty, recommending model capacity increases over excessive repetition and showing that strong weight decay reduces the penalty coefficient by ~70%.