---
id: 20261027-T0-08
title: "Muon优化器何时起作用？梯度噪声结构决定预条件收益"
title_en: "When Does Muon Precondition? Structured Gradient Noise Shapes Its Benefits"
url: https://ai.daily.yangsir.net/daily/20261027-T0-08
issue_date: 2026-10-27
publish_date: 2026-10-26T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2610.31728
---

# Muon优化器何时起作用？梯度噪声结构决定预条件收益

Muon已成为大语言模型训练中流行的优化器，但其理论基础仍不清楚。这项研究从梯度噪声的几何结构入手，从理论和实验两方面分析Muon的预条件收益从何而来。结论指向噪声的结构性：当梯度噪声具备特定结构时，预条件才带来明显好处。对训练大模型的团队来说，这提供了判断何时该用Muon、何时收益有限的依据。论文为arXiv预印本。

## English Version

**When Does Muon Precondition? Structured Gradient Noise Shapes Its Benefits**

Muon has become a popular optimizer for large language model training, yet its theoretical foundations remain poorly understood. This work studies how the geometry of gradient noise shapes Muon's preconditioning benefits, analyzing the question both theoretically and empirically. The results point to noise structure: preconditioning pays off when gradient noise has particular structure. For teams training large models, this offers a basis for judging when Muon helps and when gains are limited. The paper is an arXiv preprint.

---

**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2610.31728)

**详情页**：https://ai.daily.yangsir.net/daily/20261027-T0-08

---

*智语观潮 · Daily — https://ai.daily.yangsir.net/llms.txt*