arXiv cs.AI· M. Harper Langston, Pierre-David Letourneau, Dalton Jones, Roland Memisevic, Peter Scott, Mingu Lee, Richard A. Lethin, Christopher Lott, Fatih Porikli, Harris Teague·· 3 小时前AI 评分34
学习型优化器调度中乘法与加法状态耦合方式对比研究
Multiplicative vs. Additive State Coupling in Learned Optimizer Schedules
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arXiv 论文研究学习型优化器调度中求解器状态的耦合方式：通过对 MAX-CUT 连续惩罚松弛展开经典求解器做反向传播来学习调度。
正文 · 原文
Abstract:We learn schedules for a continuous penalty relaxation of MAX-CUT by backpropagating through an unrolled classical solver. A state-blind schedule generalizes to instances substantially larger than those used for training. A state-adaptive bilinear schedule also generalizes out of distribution, outperforming its untrained baseline across 15 seeds ($t=4.1$). By contrast, an additive ablation with identical inputs and output heads is statistically indistinguishable from the bilinear model in distribution but performs worse than its own baseline out of distribution ($t=-11.7$). This separation appears without regularization and persists across a $30$ round weight-decay sweep: the bilinear advantage remains significant at every tested value, whereas the additive model never recovers. The results in this setting isolate how solver state enters the schedule, rather than state adaptivity alone, as the architectural distinction associated with out-of-distribution transfer.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Numerical Analysis (math.NA); Optimization and Control (math.OC) |
| Cite as: | arXiv:2610.32801 [cs.LG] |
| (or arXiv:2610.32801v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32801 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Neurips OPT-ML 2026 Workshop |
Submission history
From: M. Harper Langston [view email]
[v1]
Fri, 23 Oct 2026 15:40:23 UTC (1,177 KB)
来源：arXiv cs.AI · arxiv.org