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News/Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training
arxiv
PublishedSeptember 18, 2026 at 4:00 AM
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Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training

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arXiv:2509.11983v3 Announce Type: replace Abstract: Neural network (NN) training is inherently a large-scale matrix optimization problem, yet the matrix structure of NN parameters has long been overlooked. Recently, the optimizer Muon \citep{jordanmuon}, which explicitly exploits this structure, has

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