Abstract:MLP is a heavily used backbone in modern deep learning (DL) architectures for supervised learning on tabular data, and AdamW is the go-to optimizer used to train tabular DL models. Unlike architecture design, however, the choice of optimizer for tabular DL has not been examined systematically, despite new optimizers showing promise in other domains. To fill this gap, we benchmark 15 optimizers on 17 tabular datasets for training MLP-based models in the standard supervised learning setting under a shared experiment protocol. Our main finding is that the Muon optimizer consistently outperforms AdamW, and thus should be considered a strong and practical choice for practitioners and researchers, if the associated training efficiency overhead is affordable. Additionally, we find exponential moving average of model weights to be a simple yet effective technique that improves AdamW on vanilla MLPs, though its effect is less consistent across model variants.
| Comments: | Code: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.15297 [cs.LG] |
| (or arXiv:2604.15297v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.15297 arXiv-issued DOI via DataCite |
From: Yury Gorishniy [view email]
[v1]
Thu, 16 Apr 2026 17:57:02 UTC (242 KB)
[v2]
Fri, 17 Apr 2026 17:48:55 UTC (245 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。