Abstract
Recent progress in Large Language Models (LLMs) has opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS). However, existing LLM-driven NAS approaches rely heavily on prompt engineering and domain-specific tuning, limiting their practicality and scalability across diverse tasks. In this work, we propose LM-Searcher, a novel framework that leverages LLMs for cross-domain neural architecture optimization without the need for extensive domain-specific adaptation. Central to our approach is NCode, a universal numerical string representation for neural architectures, which enables cross-domain architecture encoding and search. We also reformulate the NAS problem as a ranking task, training LLMs to select high-performing architectures from candidate pools using instruction-tuning samples derived from a novel pruning-based subspace sampling strategy. Our curated dataset, encompassing a wide range of architecture-performance pairs, encourages robust and transferable learning. Comprehensive experiments demonstrate that LM-Searcher achieves competitive performance in both in-domain (e.g., CNNs for image classification) and out-of-domain (e.g., LoRA configurations for segmentation and generation) tasks, establishing a new paradigm for flexible and generalizable LLM-based architecture search. The datasets and models will be released at this https URL.
Abstract (translated)
最近在大型语言模型（LLMs）方面的进展为解决复杂的优化问题开辟了新的途径，其中包括神经架构搜索（NAS）。然而，现有的基于LLM的NAS方法高度依赖于提示工程和特定领域的调整，这限制了它们在各种任务中的实用性和可扩展性。为此，我们提出了一种新型框架LM-Searcher，它利用大型语言模型进行跨领域神经架构优化，并且无需广泛的领域特定适应。 我们的方法核心在于NCode，这是一种用于表示神经架构的通用数值字符串表示法，它可以实现跨领域的架构编码和搜索。此外，我们将NAS问题重新表述为一个排序任务，通过使用一种基于剪枝的新颖子空间采样策略获得的指令微调样本训练LLM，来从候选池中选择高性能的架构。 我们精心策划的数据集涵盖了广泛的架构-性能对组合，鼓励了稳健且可迁移的学习。全面的实验表明，LM-Searcher在领域内（例如用于图像分类的CNNs）和跨领域任务（如分割和生成中的LoRA配置）上均实现了具有竞争力的表现，从而确立了一种基于LLM的新灵活通用架构搜索范例。 相关数据集与模型将会在此链接处发布：[提供具体的URL]。
URL
https://arxiv.org/abs/2509.05657
