Predictable scaling relies on developing deep learning infrastructure and optimization methods that ensure model behavior follows consistent mathematical trajectories across multiple orders of magnitude. In the training of GPT-4, aspects of final performance—such as codebase next-word prediction loss and coding problem pass rates—were accurately predicted prior to full training by fitting power-law scaling functions to smaller models trained with as little as 1/1,000th the compute budget.

Predictable Scaling in GPT-4

University of Michigan - Ann Arbor

Ch.1 Scaling Dynamics - Frontier Model Dynamics: Scaling Laws, Calibration, and Post-Training Alignment @ University of Michigan - Ann Arbor

Predictable Scaling Laws and Performance Extrapolation - Frontier Model Dynamics: Scaling Laws, Calibration, and Post-Training Alignment @ University of Michigan - Ann Arbor

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