Optimistic Dual Averaging Unifies Modern Optimizers
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Computer Science > Machine Learning
Title:Optimistic Dual Averaging Unifies Modern Optimizers
Abstract:We introduce SODA, a generalization of Optimistic Dual Averaging, which provides a common perspective on state-of-the-art optimizers like Muon, Lion, AdEMAMix and NAdam, showing that they can all be viewed as optimistic instances of this framework. Based on this framing, we propose a practical SODA wrapper for any base optimizer that eliminates weight decay tuning through a theoretically-grounded $1/k$ decay schedule. Empirical results across various scales and training horizons show that SODA consistently improves performance without any additional hyperparameter tuning.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.11172 [cs.LG] |
| (or arXiv:2605.11172v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.11172
arXiv-issued DOI via DataCite (pending registration)
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