arXiv:2606.13867v1 Announce Type: new Abstract: Muon is an increasingly widely used optimizer that replaces a gradient $G=USV^\top$ with its polar factor $UV^\top$, thereby flattening the singular spectrum. However, full flattening discards singular-value information that may matter for adaptation. We introduce Muon$^p$, a Muon-style optimizer that instead uses fractional spectral-power updates $US^pV^\top$ for rational $p\in(0,1)$, interpolating between Muon and gradient descent. To make it practical, we prove that fractional spectral powers cannot be computed by any fixed univariate polynomi
The continuous evolution of AI optimization techniques drives research into more efficient and adaptable algorithms, with Muon gaining traction as a new baseline.
Improved optimizers like Muon^p can significantly enhance the speed, stability, and performance of AI models, leading to more capable and efficient AI systems for various applications.
Optimizers now have a new, more adaptable method for spectral manipulation, allowing for a finer balance between gradient descent and 'flattened' spectrum approaches in AI training.
- · AI researchers
- · Deep learning practitioners
- · SaaS companies
- · Hardware providers
- · Inefficient AI training processes
- · Less adaptive optimization methods
AI models will train more efficiently and potentially achieve better performance across various tasks.
Faster and more robust AI development could accelerate advancements in AI agents and other complex AI systems.
The increased efficiency in AI model training could slightly reduce the compute and energy requirements per model, though overall demand will continue to grow.
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Read at arXiv cs.LG