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News/Worker Disagreement Reveals Sharp Directions in Local SGD
arxiv
PublishedMay 28, 2026 at 4:00 AM
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Worker Disagreement Reveals Sharp Directions in Local SGD

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Publisher summary· verbatim

arXiv:2605.27739v1 Announce Type: cross Abstract: Deep neural network training often exhibits highly anisotropic loss geometry, where a few sharp dominant Hessian directions coexist with a large flatter bulk. Gradients tend to align disproportionately with these dominant directions, although stable

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#machine-learning#deep-learning#optimization#hessian-matrix

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Mentioned models
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    MLPs
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    Transformers
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Read original ↗All from arxiv →
Tags
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#machine-learning#deep-learning#optimization#hessian-matrix

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