arXiv:2605.26842v1 Announce Type: new Abstract: The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to produce geometry-aware updates. However, like all first-order methods, Muon can become trapped in sharp local minima. In this work, we present MONA, an optimizer that bridges Muon's orthogonalization framework with curvature-aware acceleration. MONA adds an acceleration term directly into Muon's gradient processing pipeline. This term is calculated from the exponential moving average of gradient differ
The continuous drive for more efficient and powerful large language models necessitates ongoing innovation in optimization algorithms, pushing for advancements like MONA to overcome limitations of existing methods.
Improved optimizer performance directly translates to faster and more resource-efficient training of large language models, significantly impacting the cost and scalability of AI development.
The introduction of MONA offers a potentially more stable and efficient training pathway for large language models by combining geometry-aware updates with curvature-aware acceleration, addressing common optimization challenges.
- · AI research labs
- · Large language model developers
- · Cloud AI providers
- · Compute hardware manufacturers
- · Less efficient optimizer developers
- · Organizations with limited compute budgets relying on older methods
Faster and more stable training of increasingly complex large language models becomes feasible.
Reduced training costs and time could democratize access to advanced AI model development for a wider range of institutions.
The development of even larger and more capable AI models accelerates, pushing the boundaries of what AI can achieve.
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Read at arXiv cs.LG