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As language models scale up, it becomes increasingly expensive to verifyresearch ideas because conclusions on small models do not trivially transfer tolarge ones. A possible solution is to establish a generic system that directlypredicts some metrics for large models solely based on the results andhyperparameters from small models. Existing methods based on scaling lawsrequire hyperparameter search on the largest models, which is impractical withlimited resources. We address this issue by presenting our discoveriesindicating that Maximal Update parametrization (Mup) enables accurate fittingof scaling laws for hyperparameters close to common loss basins, without anysearch. Thus, different models can be directly compared on large scales withloss prediction even before the training starts. We propose a new paradigm as afirst step towards reliable academic research for any model scale without heavycomputation. Code is publicly available athttps://github.com/cofe-ai/Mu-scaling.

Research without Re-search: Maximal Update Parametrization Yields Accurate Loss Prediction across Scales