Musec: MomentUm SpEctral Clipping for Stable Muon-type Training
Muon has emerged as a highly effective optimizer for large language model training, often achieving superior convergence and performance compared with the widely adopted Adam and AdamW optimizers. Nevertheless, Muon is prone to training instability due to its spectral flattening, manifested by loss spikes and unbounded growth of model weights. Existing approaches primarily rely on weight or attention-logit clipping, which require architecture-specific modifications and do not directly address instability across all model components. We propose MomentUm SpEctral Clipping (Musec), which replaces Muon's spectral flattening with spectral clipping: rather than setting all singular values of th...
| Authors | Zhuanghua Liu, Menglian Wang, Luo Luo |
|---|---|
| Primary category | cs.LG |
| Published | 10 September 2026 |
| Last updated at source | 11 September 2026 |
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