arXiv:2606.09658v1 Announce Type: new Abstract: Muon has recently emerged as a state-of-the-art optimizer for pretraining Large Language Models (LLMs) and vision classifiers. Despite its efficiency advantage over Adam and SGD, the feature-learning advantage of Muon remains unclear. This paper investigates Muon's feature-learning advantage through the lens of robustness and transferability. First, by evaluating pretrained models on corrupted images and texts, we show that features learned by Muon are consistently more robust than those learned by Adam and SGD across different architectures, inc
This research provides a clearer understanding of Muon's advantage in feature learning, building on its known efficiency in pretraining LLMs and vision classifiers.
Improved robustness and transferability of features learned by optimizers directly impacts the reliability, applicability, and data efficiency of advanced AI models.
Optimizers like Muon are shown to fundamentally alter feature quality, suggesting a new front in AI model development beyond just architecture and scale.
- · AI model developers
- · Cloud computing providers
- · Industries deploying LLMs
- · Developers solely relying on Adam
Wider adoption of Muon or similar advanced optimizers for training AI models.
Reduced need for extensive fine-tuning and specialized datasets due to more robust and transferable learned features.
Accelerated development and deployment of AI in resource-constrained or novel application environments.
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Read at arXiv cs.LG