Evolutionary Negative Module Pruning for Better LoRA Merging
Anda Cao Zhejiang University, Zhuo Gou Zhejiang University, Yi Wang Zhejiang University, Kaixuan Chen, Yu Wang Zhejiang University, Can Wang, Mingli Song Zhejiang University, Jie Song Zhejiang University
原文摘要
Merging multiple Low-Rank Adaptation (LoRA) experts into a single backbone is a promising approach for efficient multi-task deployment. While existing methods strive to alleviate interference via weight interpolation or subspace alignment, they rest upon the implicit assumption that all LoRA matrices contribute constructively to the merged model. In this paper, we uncover a critical bottleneck in current merging paradigms: the existence of \textit{negative modules}—specific LoRA layers that inherently degrade global performance upon merging. We propose \textbf{E}volutionary \textbf{N}egative \textbf{M}odule \textbf{P}runing (\textbf{ENMP}), a plug-and-play LoRA pruning method to locate and exclude these detrimental modules prior to merging. By leveraging an evolutionary search strategy, ENMP effectively navigates the discrete, non-differentiable landscape of module selection to identify optimal pruning configurations. Extensive evaluations demonstrate that ENMP consistently boosts the performance of existing merging algorithms, achieving a new state-of-the-art across both language and vision domains. Code is available at https://github.com/CaoAnda/ENMP-LoRAMerging.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表