Skaling: Chinchilla's Exponents Meet Kaplan's Coupling
Summary
The paper introduces the Skaling law, a generalized neural scaling law that couples model capacity and data through an interaction exponent, reducing prediction error by 1.5-3x and enabling full-grid extrapolation using roughly 10x less compute.
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Paper page - Skaling: Chinchilla’s Exponents Meet Kaplan’s Coupling
Source: https://huggingface.co/papers/2608.07222
Abstract
Neuralscalinglawsarefoundationalforlanguagemodeldevelopment,yetstandardformulationssystematicallyunder-andoverestimatelossatdata-scarceandovertrainingextremes.Thisfailureoriginatesintheunderlyingassumptionthatmodelsizeandtrainingdataimpactthelossindependently.Toaddressthis,weintroducetheSkalinglaw,ageneralizedfunctionalformthatcouplesmodelcapacityanddatathroughasingleinteractionexponent.ThissimpleextensionreducestheMeanAbsolutePercentageError(MAPE)by1.5-3xacrossbothinterpolationandextrapolationregimes.Whenpairedwithasparsegridstrategyrestrictedtolow-computeregimes,theSkalinglawachievesaccuratefull-gridextrapolationusingapproximately10xlesscomputethanuniformsweeps.Byenablingreliableperformancepredictionfromsmall-scaleexperiments,theSkalinglawprovidesamorerobustandresource-efficientframeworkforallocatingcomputebudgetsinnext-generationmodeltraining.
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