Papers › u-$μ$P: The Unit-Scaled Maximal Update Parametrization

u-$μ$P: The Unit-Scaled Maximal Update Parametrization

24 Jul 2024arXiv:2407.17465archive 2025-07-28

Charlie Blake, Constantin Eichenberg, Josef Dean, Lukas Balles, Luke Y. Prince, Björn Deiseroth, Andres Felipe Cruz-Salinas, Carlo Luschi, Samuel Weinbach, Douglas Orr

The Maximal Update Parametrization (μP) aims to make the optimal hyperparameters (HPs) of a model independent of its size, allowing them to be swept using a cheap proxy model rather than the full-size target model. We present a new scheme, u-μP, which improves upon μP by combining it with Unit Scaling, a method for designing models that makes them easy to train in low-precision. The two techniques have a natural affinity: μP ensures that the scale of activations is independent of model size, and Unit Scaling ensures that activations, weights and gradients begin training with a scale of one. This synthesis opens the door to a simpler scheme, whose default values are near-optimal. This in turn facilitates a more efficient sweeping strategy, with u-μP models reaching a loss that is equal to or lower than comparable μP models and working out-of-the-box in FP8.

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