Analyzing Scaling Laws for Compute, Model Size, and Data Allocation
June 26, 2026
Scaling laws describe the predictable power-law relationship between training loss, model size, dataset size, and compute. The analysis explores different methodology frameworks, such as fixing model sizes to vary token budgets or using IsoFLOP profiles, to optimize compute allocation between parameters and data.
HOW THIS AFFECTS YOU
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researcherYou can use these parametric fits to better estimate the requirements for scaling training runs.
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founderThis provides a framework for deciding whether to invest compute in increasing model parameters or dataset tokens.