English

Convergence of Muon with Newton-Schulz

Machine Learning 2026-01-28 v1 Machine Learning Optimization and Control

Abstract

We analyze Muon as originally proposed and used in practice -- using the momentum orthogonalization with a few Newton-Schulz steps. The prior theoretical results replace this key step in Muon with an exact SVD-based polar factor. We prove that Muon with Newton-Schulz converges to a stationary point at the same rate as the SVD-polar idealization, up to a constant factor for a given number qq of Newton-Schulz steps. We further analyze this constant factor and prove that it converges to 1 doubly exponentially in qq and improves with the degree of the polynomial used in Newton-Schulz for approximating the orthogonalization direction. We also prove that Muon removes the typical square-root-of-rank loss compared to its vector-based counterpart, SGD with momentum. Our results explain why Muon with a few low-degree Newton-Schulz steps matches exact-polar (SVD) behavior at a much faster wall-clock time and explain how much momentum matrix orthogonalization via Newton-Schulz benefits over the vector-based optimizer. Overall, our theory justifies the practical Newton-Schulz design of Muon, narrowing its practice-theory gap.

Keywords

Cite

@article{arxiv.2601.19156,
  title  = {Convergence of Muon with Newton-Schulz},
  author = {Gyu Yeol Kim and Min-hwan Oh},
  journal= {arXiv preprint arXiv:2601.19156},
  year   = {2026}
}

Comments

Accepted at ICLR 2026