Edge of Stability Selectively Shapes Learning Across the Data Distribution

Abstract:Existing analyses of the edge of stability (EoS) treat it as a global property of optimization. We show that it is also selective: the stability constraint redistributes learning across subsets of the training distribution, amplifying progress on some groups while suppressing progress on others. Using a branching intervention that enters or exits the EoS regime from the same training state, we causally demonstrate this trade-off and identify two necessary conditions for a group to benefit. First, its aggregate gradient must align with the top Hessian eigenvector. We isolate this mechanism with a controlled perturbation that preserves distance but randomizes direction, destroying alignment and eliminating the advantage. Second, the group must sustain non-vanishing gradient magnitude over time. Under cross-entropy loss, gradient saturation decouples confidently classified groups, shifting the advantage to output-outliers, whose gradients persist. Together, these results show that EoS functions not only as a stability boundary, but as a mechanism governing the allocation of learning across the data distribution.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2606.04212 [cs.LG]

(or arXiv:2606.04212v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.04212 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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