arXiv Machine Learning
Jul 15

AMUSE: Anytime Muon with Stable Gradient Evaluation

arXiv:2605. 22432v2 Announce Type: replace Abstract: Modern deep learning commonly relies on AdamW with prescribed learning rate schedules, but recent works challenge both components: Schedule-Free optimization removes explicit schedules via iterate averaging, and Muon improves the update geometry by orthogonalizing momentum for matrix parameters.

By Jueun Kim, Baekrok Shin, Jihun Yun, Beomhan Baek, Minhak Song, Chulhee Yun
arXiv AI
Jun 12

LoRA-Muon: Spectral Steepest Descent on the Low-Rank Manifold

arXiv:2606. 12921v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) significantly reduces compute and memory costs for finetuning Deep Learning models but is often harder to tune than dense training: when using factor-wise optimizers such as AdamW, it is sensitive to initialization choices, its optimal learning rates transfer poorly across ranks, and it often fails to beat dense baselines.

By Franz Louis Cesista, Katherine Crowson, C\'edric Simal, Stella Biderman
arXiv Machine Learning
Sep 22

Muon Can Outperform Dedicated Continual Learning Methods

arXiv:2609.24678v1 Announce Type: new Abstract: Continual learning with Low-Rank Adapters (LoRA) typically mitigates forgetting by penalizing the overlap between a new update and the accumulated past...

By Sebastian George Sincari (Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania), Bogdan Alexandru Gheorghe (Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania), Antonio Barbalau (Bitdefender, Bucharest, Romania)