@inproceedings{lin-etal-2026-lazymuon,
    title = "{L}azy{M}uon: Exploiting Angular Stability to Reduce Newton-Schulz Overhead in Muon for Efficient Large Model Training",
    author = "Lin, Kainan  and
      Lu, Rongwei  and
      Dong, Haotian  and
      Chen, Le  and
      Jiang, Jingyan  and
      Yang, Huan  and
      Wang, Zhi",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.173/",
    pages = "3646--3659",
    ISBN = "979-8-89176-462-0"
}