MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation
Lu Li Tianyu Zhang Zhiqi Bu Suyuchen Wang Huan He Jie Fu Yonghui Wu Jiang Bian Yong Chen Yoshua Bengio
Model merging has emerged as an effective approach to combining multiple single-task models into a multitask model. This process typically involves computing a weighted average of the model parameters without additional training. Existing model-merging methods focus on improving average task accuracy. However, interference and conflicts between the objectives of different tasks can lead to trade-offs during the merging process. In real-world applications, a set of solutions with various trade-offs can be more informative, helping practitioners make decisions based on diverse preferences. In this paper, we introduce a novel and low-compute algorithm, Model Merging with Amortized Pareto Front (MAP). MAP efficiently identifies a Pareto set of scaling coefficients for merging multiple models, reflecting the trade-offs involved. It amortizes the substantial computational cost of evaluations needed to estimate the Pareto front by using quadratic approximation surrogate models derived from a preselected set of scaling coefficients. Experimental results on vision and natural language processing tasks demonstrate that MAP can accurately identify the Pareto front, providing practitioners with flexible solutions to balance competing task objectives. We also introduce Bayesian MAP for scenarios with a relatively low number of tasks and Nested MAP for situations with a high number of tasks, further reducing the computational cost of evaluation.
model merging, transfer learning, multitask learning, task arithmetic, multi-objective optimization
We provide a computation-efficient algorithm for finding the Pareto front representing the trade-offs during model merging caused by conflicting objectives between different tasks.