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Neural scaling laws define a predictable relationship between a model'sparameter count and its performance after training in the form of a power law.However, most research to date has not explicitly investigated whether scalinglaws can be used to accelerate model development. In this work, we perform suchan empirical investigation across a wide range of language understanding tasks,starting from models with as few as 10K parameters, and evaluate downstreamperformance across 9 language understanding tasks. We find that scaling lawsemerge at finetuning time in some NLP tasks, and that they can also beexploited for debugging convergence when training large models. Moreover, fortasks where scaling laws exist, they can be used to predict the performance oflarger models, which enables effective model selection. However, revealingscaling laws requires careful hyperparameter tuning and multiple runs for thepurpose of uncertainty estimation, which incurs additional overhead, partiallyoffsetting the computational benefits.

Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments