DMuon: Efficient Distributed Muon Training with Near-Adam Overhead
Matrix-orthogonalization-based optimizers, exemplified by Muon, have demonstrated strong convergence behavior across a wide range of modern deep learning workloads. The matrix-aware updates offer a compelling alternative to conventional element-wise optimization, particularly as model architectures continue to grow in scale and heterogeneity. Yet contemporary distributed training infrastructure built around the assumption of element-wise optimizers is poorly matched to matrix-level optimizers such as Muon, whose updates couple entire weight matrices and require costly Newton-Schulz iterations. Vanilla Muon implementations incur more than 2x the cost of forward and backward passes. To clos...
| Authors | Vincent Chen, Starrick Liu, Regis Cheng et al. |
|---|---|
| Primary category | cs.DC |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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