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Reassessing Muon for Matrix Factorization
A new arXiv paper critically reassesses the optimizer Muon by testing it on low-rank matrix factorization—a controlled, spectrally structured problem—finding its reported advantages over AdamW are inconsistent and highly sensitive to hyperparameters, challenging assumptions about its inherent superiority.
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"New study finds Muon optimizer does not consistently outperform AdamW on matrix factorization, suggesting its LLM advantages may depend on context rather than intrinsic superiority."
arXiv Machine Learning
Jul 16, 2026