Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos

Lucas Fernandez-Sarmiento
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:30825-30860, 2026.

Abstract

We develop a mean-field theory of dropout as a perturbation of critical signal propagation at the edge of chaos, and show that it predicts a simple, no-cost change to standard practice: front-loaded dropout schedules cut test loss by 18–35% over constant dropout in MLPs and Vision Transformers at fixed budget. The theoretical mechanism is that dropout shifts the perfect-alignment fixed point, making the depth scale for information propagation finite even at critical initialization. We derive critical and crossover scaling laws for correlation decay and establish that smooth activations and kinked, ReLU-like activations constitute distinct universality classes, with different critical exponents and a universal two-parameter scaling collapse in detuning and dropout strength. The distinction traces to the analytic structure of the correlation map: smooth activations admit a Taylor expansion near perfect alignment, while kinked activations develop a branch point with universal non-analyticity. As a corollary, the framework yields saturated dropout profiles under fixed budget; a regularization-reach argument then selects front-loaded schedules, with accuracy gains as a consistent secondary effect. We also discuss how the same Gaussian-kernel structure extends the theory beyond MLPs toward CNNs and residual architectures.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-fernandez-sarmiento26a, title = {Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos}, author = {Fernandez-Sarmiento, Lucas}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {30825--30860}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/fernandez-sarmiento26a/fernandez-sarmiento26a.pdf}, url = {https://proceedings.mlr.press/v306/fernandez-sarmiento26a.html}, abstract = {We develop a mean-field theory of dropout as a perturbation of critical signal propagation at the edge of chaos, and show that it predicts a simple, no-cost change to standard practice: front-loaded dropout schedules cut test loss by 18–35% over constant dropout in MLPs and Vision Transformers at fixed budget. The theoretical mechanism is that dropout shifts the perfect-alignment fixed point, making the depth scale for information propagation finite even at critical initialization. We derive critical and crossover scaling laws for correlation decay and establish that smooth activations and kinked, ReLU-like activations constitute distinct universality classes, with different critical exponents and a universal two-parameter scaling collapse in detuning and dropout strength. The distinction traces to the analytic structure of the correlation map: smooth activations admit a Taylor expansion near perfect alignment, while kinked activations develop a branch point with universal non-analyticity. As a corollary, the framework yields saturated dropout profiles under fixed budget; a regularization-reach argument then selects front-loaded schedules, with accuracy gains as a consistent secondary effect. We also discuss how the same Gaussian-kernel structure extends the theory beyond MLPs toward CNNs and residual architectures.} }
Endnote
%0 Conference Paper %T Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos %A Lucas Fernandez-Sarmiento %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-fernandez-sarmiento26a %I PMLR %P 30825--30860 %U https://proceedings.mlr.press/v306/fernandez-sarmiento26a.html %V 306 %X We develop a mean-field theory of dropout as a perturbation of critical signal propagation at the edge of chaos, and show that it predicts a simple, no-cost change to standard practice: front-loaded dropout schedules cut test loss by 18–35% over constant dropout in MLPs and Vision Transformers at fixed budget. The theoretical mechanism is that dropout shifts the perfect-alignment fixed point, making the depth scale for information propagation finite even at critical initialization. We derive critical and crossover scaling laws for correlation decay and establish that smooth activations and kinked, ReLU-like activations constitute distinct universality classes, with different critical exponents and a universal two-parameter scaling collapse in detuning and dropout strength. The distinction traces to the analytic structure of the correlation map: smooth activations admit a Taylor expansion near perfect alignment, while kinked activations develop a branch point with universal non-analyticity. As a corollary, the framework yields saturated dropout profiles under fixed budget; a regularization-reach argument then selects front-loaded schedules, with accuracy gains as a consistent secondary effect. We also discuss how the same Gaussian-kernel structure extends the theory beyond MLPs toward CNNs and residual architectures.
APA
Fernandez-Sarmiento, L.. (2026). Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:30825-30860 Available from https://proceedings.mlr.press/v306/fernandez-sarmiento26a.html.

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