arXiv:2605.24770v1 Announce Type: new Abstract: Muon is a recently developed matrix-aware optimizer that has shown strong results in transformer training, but its behavior in vision transformers (ViTs) is not yet well understood. We study Muon for ViT training, largely on ImageNet-100 and Pl@ntNet-300K, comparing against AdamW under standard vision recipes involving mixup, cutmix, smoothing, and random augmentation and erasing. Muon consistently outperforms AdamW, with especially large gains on long-tailed Pl@ntNet macro top-1. These gains are also recipe-dependent, where Muon benefits much mo
The continuous evolution of AI models, particularly Vision Transformers, necessitates ongoing research into more efficient and effective optimization techniques to handle increasing complexity and data volumes.
Improved optimizers like Muon can significantly enhance the training efficiency and performance of Vision Transformers, leading to more capable AI systems with less computational overhead.
The landscape of ViT training might shift towards matrix-aware optimizers, potentially accelerating AI development and deployment for vision-related tasks.
- · AI researchers
- · Companies deploying vision AI
- · Hardware manufacturers for AI (indirectly through demand for efficient models)
- · Suboptimal AI training methods
- · Companies reliant on less efficient optimization algorithms
Wider adoption of Muon or similar advanced optimizers for Vision Transformer training.
Reduced training times and computational costs for developing high-performance vision AI models.
Acceleration of new vision AI applications and capabilities due to enhanced model performance and efficiency.
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