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Flat Loss, Evolving Features: Grokking in Modular Addition

arXiv:2610.22593v1 Announce Type: new Abstract: Can a network keep learning while its training cross-entropy stays nearly flat at a positive level? We show that it can. On modular addition, a standard testbed for grokking, ReLU networks trained with supported-Polar hidden updates, normalized output…

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  1. 2026-10-19 04:00 · arXiv cs.LG
    Flat Loss, Evolving Features: Grokking in Modular Addition

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