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When Muon Meets Task Interference: A Spectral Perspective on Continual Learning and Model Merging
Author1, Author2, Author3, Author4, Author5
continual learningmodel mergingoptimizationtask interference
2608.27518
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2h ago80%
Abstract
This paper explores the shared phenomenon of task interference in continual learning and model merging, proposing the Muon optimizer as a solution.
Reality Card
Core Claim
The Muon optimizer improves accuracy by up to +5.02 points on model-merging benchmarks and delivers consistent gains in continual learning tasks.
Method / Result
Replacing the AdamW optimizer with Muon improves accuracy by up to +5.02 points.
Limitations
The paper does not address potential limitations in the generalizability of the Muon optimizer across all tasks.
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