arXiv:2606.04212v1 Announce Type: new 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 Hes
This paper offers a novel analytical perspective on the 'edge of stability' in AI optimization, a rapidly evolving area of research refining how large models learn.
Understanding the selective nature of the edge of stability provides a more nuanced view of AI training dynamics, impacting hardware utilization, model fairness, and training efficiency.
The previous global understanding of the edge of stability is refined to a selective property, implying that specific data subsets are differentially impacted during training.
- · AI researchers
- · Model developers specializing in fairness
- · Hardware developers optimizing for specific training regimes
- · Developers relying solely on global optimization heuristics
More targeted optimization strategies could emerge to address learning inequities across data distributions.
This could lead to new architectures or training algorithms designed to balance learning across diverse datasets.
Improved understanding of model bias originating from optimization dynamics, potentially leading to more robust and ethical AI systems.
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Read at arXiv cs.LG