Beyond Uniform Subspaces: Spectrum-Aware and Depth-Adaptive Fusion for Multi-Task Model Merging

2026-09-21

AI Summary

为了解决多任务模型合并时忽视任务间固有异质性的问题，提出SADA-Merging方法，通过谱感知和深度自适应的方式进行无数据模型融合。

Abstract

Model merging aims to consolidate multiple task-specific models without access to extra training process. However, existing subspace-based methods largely rely on a uniform treatment of task updates, overlooking their intrinsic spectral and depth-wise heterogeneity. We identify two key deviations from this assumption: different tasks require different subspace capacity and exhibit different tolerance to spectral transformation, while subspace projection introduces depth-dependent distortion. Based on these observations, we propose SADA-Merging, a spectrum-aware and depth-adaptive framework for data-free model merging. SADA-Merging allocates task-specific subspace capacity according to spectral complexity, adapts spectral preservation according to task-wise plasticity, and applies depth-dependent anchoring to compensate for projection-induced distortion. This enables the fusion process to adapt to both the intrinsic geometry of each task and its sensitivity across network depth. SADA-Merging operates directly on task updates and is applicable to both full fine-tuning and LoRA settings. Extensive experiments demonstrate consistent improvements over existing data-free merging methods across different task scales and adaptation settings.

Problem

• Model Merging
• Subspace Capacity
• Spectral Transformation
• Depth-Dependent Distortion

Innovation

• Spectrum-Aware
• Depth-Adaptive
• Model Merging
• Subspace Capacity
• Projection-Induced Distortion