SPIN Unprocessed October 27, 2026 ai_technology research
When and How Does Muon Precondition? The Role of Structured Gradient Noise
View original on arxiv.orgOverview
arXiv:2610.31728v1 Announce Type: new Abstract: Muon has become a popular optimizer for large language model training, yet its theoretical foundations remain poorly understood. In this work, we study how gradient noise geometry shapes Muon's preconditioning benefits. We first show, theoretically and empirically, that under the classical isotropic noise assumption, Muon offers no acceleration beyond a rescaling of the learning rate. This motivates us to investigate structured noise arising from m
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Machine Learning
View all →- Aligning Large Language Models with Human Feedback: Mathematical Foundations and Algorithm Design
- Cautious Conflict-Free Inverse Gradient Methods
- Rethinking Multi-View Fusion for Urban Representation Learning
- Speculative Diffusion is a Tale of Two Tradeoffs
- Nested Sparsity for Scalable Pretraining and Continual Learning
- CuraLoc: Repository-Level Fault Localization via Planned Code Inspection
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO