Second-Order Muon Done Right: A Principled Marriage of Spectral Geometry and Curvature
Not on the map. Why?
Abstract (arXiv)
Muon's polar update is exact for an unweighted spectral geometry. We introduce GO-MUON, which uses a matched data-dependent geometry and reuses it across several optimization steps. Conditioned on any positive-definite left and right maps, its raw update exactly solves the corresponding weighted spectral oracle; this statement is independent of how the maps are estimated or how recently they were refreshed. For softmax cross-entropy, we quantify when the observed-label backward factor approaches the model Fisher and generalized Gauss--Newton factor. We also show that four-step refresh nearly preserves the tracking delay of slowly changing geometry while increasing stationary factor noise, making lazy geometry a compute--statistics tradeoff rather than a denoising mechanism.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 31 references that are in the map. First posted on arXiv 10 Aug 2026.
Builds on
25 of 31 references.
References · 25
- 2015Optimizing Neural Networks with Kronecker-factored Approximate Curvaturecited by 796
- 2014New insights and perspectives on the natural gradient methodcited by 443
- 2025Muon is Scalable for LLM Trainingcited by 428
- 2018Shampoo: Preconditioned Stochastic Tensor Optimizationcited by 426
- 2019Limitations of the Empirical Fisher Approximation for Natural Gradient Descentcited by 217
- 2024Old Optimizer, New Norm: An Anthologycited by 193
- 2024SOAP: Improving and Stabilizing Shampoo using Adamcited by 192
- 2025Training Deep Learning Models with Norm-Constrained LMOscited by 192
- 2023A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scalecited by 77
- 2025Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs)cited by 72
- 2024Scalable Optimization in the Modular Normcited by 68
- 2018Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networkscited by 37
13 more
- 2025Purifying Shampoo: Investigating Shampoo's Heuristics by Decomposing its Preconditionercited by 23
- 2020Convolutional Neural Network Training with Distributed K-FACcited by 22
- 2021KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networkscited by 20
- 2026The Newton-Muon Optimizercited by 18
- 2025The Potential of Second-Order Optimization for LLMs: A Study with Full Gauss-Newtoncited by 17
- 2026Mousse: Rectifying the Geometry of Muon with Curvature-Aware Preconditioningcited by 17
- 2026MuonEq: Balancing Before Orthogonalization with Lightweight Equilibrationcited by 17
- 2026Muon2: Boosting Muon via Adaptive Second-Moment Preconditioningcited by 17
- 2025What Really Matters in Matrix-Whitening Optimizers?cited by 16
- 2026FISMO: Fisher-Structured Momentum-Orthogonalized Optimizercited by 11
- 2023Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFACcited by 9
- 2018A Coordinate-Free Construction of Scalable Natural Gradientcited by 6
- 2026MALT: Lightweight Curvature-Aware Muon via Diagonal Preconditioningcited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Beyond the Matrix Sign: Quadratic Spectral Descentcited by 1
- 2026Beyond the Matrix Sign: Quadratic Spectral Descent7 Sep 2026