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

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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Mentioned models
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arxivAdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science2harxivScoped Semantic Invariance as a Foundation for LLM Reliability2harxivGEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation2harxivEstimation of Propagated Risk in LLM-based Multi-Agent Systems2h
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