MIN-Merging: Merge the Important Neurons for Model Merging

• URL: http://arxiv.org/abs/2510.17890v2
• Date: Sun, 26 Oct 2025 11:00:04 GMT
• Title: MIN-Merging: Merge the Important Neurons for Model Merging
• Authors: Yunfei Liang,
• Abstract summary: MIN-Merging is a router-based framework that selectively merges the most important neurons to reduce such conflicts.<n>We show that MIN-Merging achieves consistent gains on in-domain tasks while retaining the generalization ability of pretrained models on out-of-domain tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Model Merging in the Essential Subspace [78.5390284258307]
  Model merging aims to integrate multiple task-specific fine-tuned models into a single multi-task model without additional training.<n>Despite extensive research, task interference remains a major obstacle that often undermines the performance of merged models.<n>We propose ESM (Essential Subspace Merging), a robust framework for effective model merging.
  arXiv  Detail & Related papers  (2026-02-23T00:33:38Z)
• Merge and Guide: Unifying Model Merging and Guided Decoding for Controllable Multi-Objective Generation [49.98025799046136]
  We introduce Merge-And-GuidE, a two-stage framework that leverages model merging for guided decoding.<n>In Stage 1, MAGE resolves a compatibility problem between the guidance and base models.<n>In Stage 2, we merge explicit and implicit value models into a unified guidance proxy, which then steers the decoding of the base model from Stage 1.
  arXiv  Detail & Related papers  (2025-10-04T11:10:07Z)
• Unraveling LoRA Interference: Orthogonal Subspaces for Robust Model Merging [38.12136955174922]
  Fine-tuning large language models (LMs) for individual tasks yields strong performance but is expensive for deployment and storage.<n>Recent works explore model merging to combine multiple task-specific models into a single multi-task model without additional training.<n>Existing merging methods often fail for models fine-tuned with low-rank adaptation (LoRA), due to significant performance degradation.
  arXiv  Detail & Related papers  (2025-05-28T23:28:12Z)
• Reinforced Model Merging [53.84354455400038]
  We present an innovative framework termed Reinforced Model Merging (RMM), which encompasses an environment and agent tailored for merging tasks.<n>By utilizing data subsets during the evaluation process, we addressed the bottleneck in the reward feedback phase, thereby accelerating RMM by up to 100 times.
  arXiv  Detail & Related papers  (2025-03-27T08:52:41Z)
• To See a World in a Spark of Neuron: Disentangling Multi-task Interference for Training-free Model Merging [16.81093103067372]
  We present the first study that relies on neuronal mechanisms for model merging.<n>We introduce NeuroMerging, a novel merging framework developed to mitigate task interference within neuronal subspaces.<n>Our findings highlighted the importance of aligning neuronal mechanisms in model merging.
  arXiv  Detail & Related papers  (2025-03-07T11:00:24Z)
• Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent [72.10987117380584]
  Merging multiple expert models offers a promising approach for performing multi-task learning without accessing their original data.<n>We find existing methods discard task-specific information that, while causing conflicts, is crucial for performance.<n>Our approach consistently outperforms previous methods, achieving state-of-the-art results across diverse architectures and tasks in both vision and NLP domains.
  arXiv  Detail & Related papers  (2025-01-02T12:45:21Z)
• Parameter Competition Balancing for Model Merging [13.66727853299506]
  PCB-Merging is a training-free technique that adjusts the coefficients of each parameter for effective model merging. PCB-Merging achieves substantial performance enhancements across multiple modalities, domains, model sizes, number of tasks, fine-tuning forms, and large language models.
  arXiv  Detail & Related papers  (2024-10-03T11:17:58Z)
• AdaMerging: Adaptive Model Merging for Multi-Task Learning [68.75885518081357]
  This paper introduces an innovative technique called Adaptive Model Merging (AdaMerging) It aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data. Compared to the current state-of-the-art task arithmetic merging scheme, AdaMerging showcases a remarkable 11% improvement in performance.
  arXiv  Detail & Related papers  (2023-10-04T04:26:33Z)
• TIES-Merging: Resolving Interference When Merging Models [95.59265307318752]
  Transfer learning can confer significant advantages, including improved downstream performance, faster convergence, and better sample efficiency. Model merging has emerged as a solution to combine multiple task-specific models into a single model without performing additional training. Existing merging methods often ignore the interference between parameters of different models, resulting in large performance drops when merging multiple models. We propose TIES-Merging, which introduces three novel steps when merging models: resetting parameters that only changed a small amount during fine-tuning, resolving sign conflicts, and merging only the parameters that are in alignment with the final agreed-upon sign.
  arXiv  Detail & Related papers  (2023-06-02T17:31:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.