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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.21169 |
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| _version_ | 1866917356617334784 |
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| author | Zhang, Chen Cheng, Yuxin Ding, Chenchen Wang, Shuqi Lei, Jingreng Yu, Runsheng WU, Yik-Chung Wong, Ngai |
| author_facet | Zhang, Chen Cheng, Yuxin Ding, Chenchen Wang, Shuqi Lei, Jingreng Yu, Runsheng WU, Yik-Chung Wong, Ngai |
| contents | Zeroth-order (ZO) optimization enables memory-efficient training of neural networks by estimating gradients via forward passes only, eliminating the need for backpropagation. However, the stochastic nature of gradient estimation significantly obscures the training dynamics, in contrast to the well-characterized behavior of first-order methods under Neural Tangent Kernel (NTK) theory. To address this, we introduce the Neural Zeroth-order Kernel (NZK) to describe model evolution in function space under ZO updates. For linear models, we prove that the expected NZK remains constant throughout training and depends explicitly on the first and second moments of the random perturbation directions. This invariance yields a closed-form expression for model evolution under squared loss. We further extend the analysis to linearized neural networks. Interpreting ZO updates as kernel gradient descent via NZK provides a novel perspective for potentially accelerating convergence. Extensive experiments across synthetic and real-world datasets (including MNIST, CIFAR-10, and Tiny ImageNet) validate our theoretical results and demonstrate acceleration when using a single shared random vector. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21169 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Model Evolution Under Zeroth-Order Optimization: A Neural Tangent Kernel Perspective Zhang, Chen Cheng, Yuxin Ding, Chenchen Wang, Shuqi Lei, Jingreng Yu, Runsheng WU, Yik-Chung Wong, Ngai Machine Learning Zeroth-order (ZO) optimization enables memory-efficient training of neural networks by estimating gradients via forward passes only, eliminating the need for backpropagation. However, the stochastic nature of gradient estimation significantly obscures the training dynamics, in contrast to the well-characterized behavior of first-order methods under Neural Tangent Kernel (NTK) theory. To address this, we introduce the Neural Zeroth-order Kernel (NZK) to describe model evolution in function space under ZO updates. For linear models, we prove that the expected NZK remains constant throughout training and depends explicitly on the first and second moments of the random perturbation directions. This invariance yields a closed-form expression for model evolution under squared loss. We further extend the analysis to linearized neural networks. Interpreting ZO updates as kernel gradient descent via NZK provides a novel perspective for potentially accelerating convergence. Extensive experiments across synthetic and real-world datasets (including MNIST, CIFAR-10, and Tiny ImageNet) validate our theoretical results and demonstrate acceleration when using a single shared random vector. |
| title | Model Evolution Under Zeroth-Order Optimization: A Neural Tangent Kernel Perspective |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.21169 |