SE-Merging: A Self-Enhanced Approach for Dynamic Model Merging

Model merging has gained increasing attention due to its intriguing property: interpolating the parameters of different task-specific fine-tuned models leads to multi-task abilities. However, despite its empirical success, the underlying mechanisms of model merging remain poorly understood. In this work, we delve into the mechanism behind model merging from a representation perspective. Our analysis reveals that model merging achieves multi-task abilities through two key capabilities: i) distinguishing samples from different tasks, and ii) adapting to the corresponding expert model for each sample. These two capabilities allow the merged model to retain task-specific expertise, enabling efficient multi-task adaptation. Building on these insights, we propose $\mathtt{\text{SE-Merging}}$, a self-enhanced model merging framework that leverages these two characteristics to dynamically identify the corresponding task for each sample and then adaptively rescales the merging coefficients to further enhance task-specific expertise in the merged model. Notably, $\mathtt{\text{SE-Merging}}$ achieves dynamic model merging without additional training. Extensive experiments demonstrate that $\mathtt{\text{SE-Merging}}$ achieves significant performance improvements while remaining compatible with existing model merging techniques.