Unified Neural Scaling Laws
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Computer Science > Machine Learning
Title:Unified Neural Scaling Laws
Abstract:We present a functional form (that we refer to as a Unified Neural Scaling Law (UNSL)) that accurately models and extrapolates the scaling behaviors of deep neural networks as multiple dimensions all vary simultaneously (i.e. how the evaluation metric of interest varies as one simultaneously varies the number of model parameters, training dataset size, number of training steps, number of inference steps, amount of compute, and various hyperparameters) for various architectures and for each of various tasks within a varied set of upstream and downstream tasks. This set includes large-scale vision, language, math, and reinforcement learning. When compared to other functional forms for neural scaling, this functional form yields extrapolations of scaling behavior that are considerably more accurate on this set.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2605.26248 [cs.LG] |
| (or arXiv:2605.26248v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26248
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Ethan Caballero V [view email][v1] Mon, 25 May 2026 18:15:27 UTC (37,868 KB)
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