Pareto Merging: Multi-Objective Optimization for Preference-Aware Model Merging

TL;DR

This paper introduces Pareto Merging, a multi-objective optimization approach that creates a set of diverse, preference-aware merged models, enabling users to select models aligned with their specific priorities, and outperforms existing methods.

Contribution

It formulates model merging as a multi-objective problem, generating a Pareto set of models that reflect different user preferences, which is a novel approach in model merging.

Findings

Produces diverse Pareto-optimal merged models

Achieves higher test accuracy than state-of-the-art baselines

Enables preference-based model selection

Abstract

Model merging, which combines multiple models into a single model, has gained popularity in recent years. By efficiently integrating the capabilities of various models, this significantly reduces the parameter count and memory usage. However, current methods can only produce one single merged model. This necessitates a performance trade-off due to conflicts among the various models, and the resultant one-size-fits-all model may not align with the preferences of different users who may prioritize certain models over others. To address this issue, we propose preference-aware model merging, and formulate this as a multi-objective optimization problem in which the performance of the merged model on each base model's task is treated as an objective. In a single merging process, the proposed parameter-efficient structure generates a Pareto set of merged models, with each representing a Pareto-optimal solution for a preference. Users can then select merged models tailored to their preferences from this learned Pareto set. Experimental results demonstrate that the proposed Pareto Merging produces diverse trade-off models and achieves higher test accuracy compared to state-of-the-art merging baselines.