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About this paper
Shengwei Wu, Yixuan Yang, Gao Huang, Song Li
The authors analyze Muon’s effect on attention learning in a one-layer Transformer for next-token prediction. Muon normalizes singular values in matrix momentum updates, and a convergence theorem shows attention updates approach the maximum normalized spectral margin under stated assumptions. In a synthetic comparison, Muon has a smaller spectral-margin gap than Adam, while Adam has a smaller Frobenius-margin gap; this does not establish better real-world language-model training.
Summary by alphaXiv.
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