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Karthik Sankaranarayanan

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#machine learning Preprint Oct 2026

Flat Loss, Evolving Features: Grokking in Modular Addition

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-layer updates and weight decay can keep 100% training accuracy while...

Akash Kumar, Karthik Sankaranarayanan · 0 citations

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