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Muon optimizer shows promise in theoretical and practical neural network training

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore the Muon optimizer, an approach designed to better handle matrix-structured parameters in neural networks. The first paper introduces a matrix-aware geometry for Sharpness-Aware Minimization (SAM), combining a spectral inner perturbation with Muon for improved robustness and validation accuracy on ImageNet-1K. The second paper provides a theoretical convergence analysis of Muon, demonstrating its potential to outperform traditional gradient descent by leveraging the low-rank structure of Hessian matrices in neural network training. AI

IMPACT The Muon optimizer's theoretical and empirical advantages could lead to more efficient and robust neural network training.

RANK_REASON Two academic papers published on arXiv detailing a new optimization method for neural networks.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Muon optimizer shows promise in theoretical and practical neural network training

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Wenzhi Zhong, Edward Milsom, Michael Murray ·

    Sharpness-Aware Minimization and Muon: Robustness under the Spectral Norm

    arXiv:2607.26001v1 Announce Type: cross Abstract: Sharpness-Aware Minimization (SAM) aims to improve generalization by encouraging insensitivity to small, worst-case parameter perturbations. However, the notion of a "small" perturbation is inherently geometry-dependent: while exi…

  2. arXiv stat.ML TIER_1 English(EN) · Wei Shen, Ruichuan Huang, Minhui Huang, Cong Shen, Jiawei Zhang ·

    On the Convergence Analysis of Muon

    arXiv:2505.23737v3 Announce Type: replace Abstract: The majority of parameters in neural networks are naturally represented as matrices. However, most commonly used optimizers treat these matrix parameters as flattened vectors during optimization, potentially overlooking their in…

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