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cs.LG2026
Muse: Representation Geometry of Muon Beyond Normalized Momentum
Da Chang, Qiankun Shi, Lvgang Zhang +4
Muon-style optimizers apply a polar map to matrix momentum, but their updates also depend on the representation of each parameter block before orthogonalization. We study this repr…
cs.LG2025
Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT
Da Chang, Peng Xue, Yu Li +3
Parameter-Efficient Fine-Tuning (PEFT) methods are crucial for adapting large pre-trained models. Among these, LoRA is considered a foundational approach. Building on this, the inf…
cs.LG2025
On the Convergence of Muon and Beyond
Da Chang, Yongxiang Liu, Ganzhao Yuan +1
The Muon optimizer has demonstrated strong empirical performance in training neural networks with matrix-structured parameters. A central theoretical question is how momentum varia…