arXiv:2606.27153v1 Announce Type: cross Abstract: 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 mat
The increasing scale and complexity of deep learning models necessitate more efficient and scalable optimization methods that go beyond traditional element-wise approaches.
Improving the efficiency of distributed training for advanced optimizers like Muon can significantly reduce the compute and energy costs of large-scale AI development, accelerating progress in the field.
The development of DMuon suggests that matrix-orthogonalization-based optimizers, which offer superior convergence, are becoming viable for distributed training setups, potentially changing the standard approach to large model optimization.
- · AI researchers and developers
- · Hyperscalers and cloud providers
- · Hardware manufacturers for AI accelerators
More efficient and faster training of large, complex AI models becomes widely accessible.
Reduced training times and costs could lead to more rapid iteration and development of novel AI architectures and applications.
The enhanced efficiency might alleviate some pressure on computational resources, indirectly impacting the energy consumption concerns associated with AI growth.
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Read at arXiv cs.LG