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arXiv · 2411.03055

ATM: Improving Model Merging by Alternating Tuning and Merging

Luca Zhou·Daniele Solombrino·Donato Crisostomi·Maria Sofia Bucarelli·Fabrizio Silvestri·Emanuele Rodolà
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Abstract

Model merging has emerged as a cost-efficient approximation to multitask learning. Among merging strategies, task arithmetic is notable for its simplicity and effectiveness. In this work, we provide a theoretical motivation for task vectors by highlighting that, under single-epoch full-batch gradient descent, they are equivalent to multitask gradients. This insight leads us to reinterpret model merging as a single step in an iterative procedure that Alternates between Tuning and Merging (ATM). We propose two applications of ATM: (1) as an alternative to multitask learning in scenarios where data sharing is restricted (e.g., federated settings), and (2) as a lightweight refinement step to improve existing model merging methods using a small validation set. Experiments across diverse vision tasks demonstrate the effectiveness of ATM.

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Luca Zhou, Daniele Solombrino, Donato Crisostomi, Maria Sofia Bucarelli, Fabrizio Silvestri, Emanuele Rodolà. 2024-11-05. ATM: Improving Model Merging by Alternating Tuning and Merging. https://arxiv.org/abs/2411.03055

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