Year: 2026
Type: conference-paper
Abstract: Muon has recently shown promising results in LLM training.In this work, we study how to further improve Muon.We argue that Muon's orthogonalized update rule suppresses the emergence of heavy-tailed weight spectra and overemphasizes the training along noise-dominated directions.Motivated by the Heavy-Tailed Self-Regularization (HT-SR) theory, we propose HTMuon.HTMuon preserves Muon's ability to capture parameter interdependencies while producing heavier-tailed updates and inducing heavier-tailed weight spectra.Experiments on LLM pretraining and image classification show that HTMuon consistently improves performance over state-of-the-art baselines and can also serve as a plug-in on top of existing Muon variants.For example, on LLaMA pretraining on the C4 dataset, HTMuon reduces perplexity by up to 0.98 compared to Muon.We further theoretically show that HTMuon corresponds to steepest descent under the Schatten-q norm constraint and provide convergence analysis in smooth non-convex settings.The implementation of HTMuon is available at https: //github.com/TDCSZ327/HTmuon. Muon 95.10 95.39 95.25 95.36 95.8 95.90 95.97 95.90 NorMuon 95.30 95.35 95.5795.24 95.74 96.03 95.91 95.75 HTMuon 95.27 95.23 95.63 95.39 96.13 95.84 96.07 95.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166821461