Muon Optimizer Reduces Generalization via Loss of Simplicity Bias
June 30, 2026
Muon improves training speed but lacks the inherent simplicity bias present in standard gradient descent. This trade-off suggests that while faster convergence is achievable, it may come at the cost of model generalization performance.
HOW THIS AFFECTS YOU
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builderEvaluating training speed alone may lead to sub-optimal production models with poor out-of-distribution performance.
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researcherYou should account for potential generalization gaps when implementing Muon in new architectures.