MIN-Merging: Merge the Important Neurons for Model Merging

Yunfei Liang

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arXiv · 2510.17890

MIN-Merging: Merge the Important Neurons for Model Merging

Yunfei Liang

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Abstract

Recent advances in deep learning have led to a surge of open-source models across diverse domains. While model merging offers a promising way to combine their strengths, existing approaches often suffer from parameter conflicts that degrade performance on domain-specific tasks. We propose MIN-Merging, a router-based framework that selectively merges the most important neurons to reduce such conflicts. Extensive experiments on Computer Vision(CV) and Natural Language Processing(NLP) benchmarks show that MIN-Merging achieves consistent gains on in-domain tasks while retaining the generalization ability of pretrained models on out-of-domain tasks. These results highlight its effectiveness as a practical solution to the parameter conflict problem in model merging.

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Yunfei Liang. 2025-10-18. MIN-Merging: Merge the Important Neurons for Model Merging. https://arxiv.org/abs/2510.17890

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