arXiv:2606.04058v1 Announce Type: new Abstract: Orthonormalized update rules have rapidly become a leading choice of optimizer for training large language models, with recent open-source state-of-the-art models adopting Muon. To keep these updates tractable, Muon performs the orthonormalization with the Newton--Schulz (NS) iteration. Since NS is only approximate, directions with small singular values fail to be orthonormalized. In Muon, NS is applied to the momentum matrix at every step, yet little is known about how the singular value spectrum of these momentum matrices behaves during trainin
The continuous evolution of large language models (LLMs) requires increasingly efficient and robust optimization techniques, with Muon representing a recent advancement in this area.
Improved optimizer understanding and performance directly impact the scalability, training cost, and ultimate capabilities of future AI models, affecting their deployment and accessibility.
This research provides deeper insight into the behavior and limitations of a state-of-the-art optimizer, potentially leading to more stable and powerful LLM training methods.
- · AI researchers
- · Large language model developers
- · Cloud computing providers
- · Open-source AI communities
- · AI models with suboptimal training stability
- · High-cost LLM training operations
More efficient and stable training of large language models becomes possible through improved optimizers.
Reduced computational costs and faster development cycles for advanced AI applications could accelerate innovation.
Enhanced LLM performance derived from better training techniques could broaden AI's economic and societal impact.
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Read at arXiv cs.LG