arXiv:2603.00742v2 Announce Type: replace Abstract: While Adam has long been the ubiquitous default optimizer for deep neural networks, Muon has recently seen rapid adoption due to its superior training speed. Although much of the literature focuses on validating the benefits of Muon, our work investigates the potential downsides of the mechanism driving this speedup. On the theoretical front, we analyze the learning dynamics of simplified Muon on deep linear networks and linear attention. Our analysis reveals that Muon gains speed by avoiding saddle points, but does so at the expense of the s
The paper is published as Muon, a new optimizer, gains rapid adoption in AI development due to perceived speed advantages over established methods like Adam.
This research provides critical theoretical and empirical validation to understand the trade-offs of using newer, faster AI optimizers, which directly impacts training efficacy and reliable model deployment.
The understanding of optimizer choices for deep neural networks shifts from a focus solely on speed to a more nuanced consideration of simplicity bias and potential downsides, influencing practitioner decisions.
- · AI researchers and practitioners
- · Organizations prioritizing model robustness
- · Developers of alternative AI optimizers
- · Organizations blindly adopting fast optimizers
- · Models trained suboptimally due to simplicity bias
AI developers will re-evaluate their choice of optimization algorithms, potentially leading to a more cautious adoption of new methods.
New research will likely emerge to mitigate the identified downsides of 'simplicity bias' in optimizers while retaining their speed benefits.
The development of more universally robust and efficient AI models could accelerate, impacting the broader capabilities and reliability of AI systems.
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Read at arXiv cs.LG