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arXiv · 2509.05656

OptiProxy-NAS: Optimization Proxy based End-to-End Neural Architecture Search

Bo Lyu·Yu Cui·Tuo Shi·Ke Li
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Abstract

Neural architecture search (NAS) is a hard computationally expensive optimization problem with a discrete, vast, and spiky search space. One of the key research efforts dedicated to this space focuses on accelerating NAS via certain proxy evaluations of neural architectures. Different from the prevalent predictor-based methods using surrogate models and differentiable architecture search via supernetworks, we propose an optimization proxy to streamline the NAS as an end-to-end optimization framework, named OptiProxy-NAS. In particular, using a proxy representation, the NAS space is reformulated to be continuous, differentiable, and smooth. Thereby, any differentiable optimization method can be applied to the gradient-based search of the relaxed architecture parameters. Our comprehensive experiments on $12$ NAS tasks of $4$ search spaces across three different domains including computer vision, natural language processing, and resource-constrained NAS fully demonstrate the superior search results and efficiency. Further experiments on low-fidelity scenarios verify the flexibility.

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Bo Lyu, Yu Cui, Tuo Shi, Ke Li. 2025-09-06. OptiProxy-NAS: Optimization Proxy based End-to-End Neural Architecture Search. https://arxiv.org/abs/2509.05656

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