Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered
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Computer Science > Machine Learning
arXiv:2605.15622 (cs)
[Submitted on 15 May 2026]
Title:Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered
Authors:Sijia Liu, Yicheng Lang, Soumyadeep Pal, Changsheng Wang, Yancheng Huang, Chongyu Fan, James Diffenderfer, Bhavya Kailkhura, Yihua Zhang
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Abstract:Zeroth-order (ZO) optimization, learning from finite differences of function evaluations without backpropagation, has recently regained attention in deep learning due to its memory efficiency and applicability to gray- or black-box pipelines. Yet, ZO methods are often dismissed as fundamentally unscalable because of estimator variance and unfavorable query complexity. We argue that this conclusion might be misguided: ZO optimization is underexplored, not underpowered. We show that many perceived limitations stem from myopic development practices, most notably full-space, element-wise, estimator-centric designs. We articulate six positions spanning the algorithmic, systems, and evaluation stack. First, we revisit the feasibility boundaries of estimator-centric ZO methods through variance control, variance-query tradeoffs, and directional-derivative lenses. Then, we identify three underexplored opportunities: (i) subspace and spectral views of ZO that enable interpretable variance reduction with graceful query scaling, (ii) the forward-only nature of ZO as a systems advantage for communication-efficient, pipeline-friendly, and resource-constrained training, and (iii) the need to de-obfuscate ZO evaluations from task complexity. We strongly advocate rethinking ZO optimization around its unique strengths and acting accordingly, opening a viable path toward large-scale, system-aware, and resource-efficient learning with ZO optimization.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.15622 [cs.LG] |
| (or arXiv:2605.15622v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15622
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered, by Sijia Liu and 8 other authors
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