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    Home»Machine Learning»[2505.04005] Iterative Orthogonalization Scaling Laws
    Machine Learning

    [2505.04005] Iterative Orthogonalization Scaling Laws

    adminBy adminNovember 11, 2026No Comments1 Min Read
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    [2505.04005] Iterative Orthogonalization Scaling Laws
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    [Submitted on 6 May 2025 (v1), last revised 10 Nov 2026 (this version, v3)]

    View a PDF of the paper titled Iterative Orthogonalization Scaling Laws, by Devan Selvaraj

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    Abstract:The muon optimizer has picked up much attention as of late as a possible replacement to the seemingly omnipresent Adam optimizer. Recently, care has been taken to document the scaling laws of hyper-parameters under muon such as weight decay and learning rate. However, at much larger scales the iterative orthogonalization procedure present in muon may suffer a possible issue as the singular values of random matrices shrink with scale. This paper shows this scaling behavior theoretically and empirically on random matrices but does not suggest what to do about it.

    Submission history

    From: Devan Selvaraj [view email]
    [v1]
    Tue, 6 May 2025 22:34:55 UTC (250 KB)
    [v2]
    Thu, 8 May 2025 03:03:40 UTC (250 KB)
    [v3]
    Tue, 10 Nov 2026 15:07:41 UTC (249 KB)

    Iterative laws Orthogonalization scaling
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