Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion

TL;DR

This paper introduces a scalable mixture of experts approach for efficiently approximating the Pareto front in multi-objective optimization of large neural networks, enabling effective trade-off analysis with minimal additional computational cost.

Contribution

The paper proposes a novel MoE-based model fusion method that captures objective trade-offs and approximates the Pareto set efficiently for large models, outperforming existing methods in scalability and resource usage.

Findings

Effectively approximates Pareto front of large models

Uses only hundreds of parameters for routers, reducing memory

Scales to multiple objectives and large model sizes

Abstract

Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost of training and evaluating models. Efficient Pareto front approximation of large models enables multi-objective optimization for various tasks such as multi-task learning and trade-off analysis. Existing algorithms for learning Pareto set, including (1) evolutionary, hypernetworks, and hypervolume-maximization methods, are computationally expensive and have restricted scalability to large models; (2) Scalarization algorithms, where a separate model is trained for each objective ray, which is inefficient for learning the entire Pareto set and fails to capture the objective trade-offs effectively. Inspired by the recent success of model merging, we propose a practical and scalable approach to Pareto set learning problem via mixture of experts (MoE) based model fusion. By ensembling the weights of specialized single-task models, the MoE module can effectively capture the trade-offs between multiple objectives and closely approximate the entire Pareto set of large neural networks. Once the routers are learned and a preference vector is set, the MoE module can be unloaded, thus no additional computational cost is introduced during inference. We conduct extensive experiments on vision and language tasks using large-scale models such as CLIP-ViT and GPT-2. The experimental results demonstrate that our method efficiently approximates the entire Pareto front of large models. Using only hundreds of trainable parameters of the MoE routers, our method even has lower memory usage compared to linear scalarization and algorithms that learn a single Pareto optimal solution, and are scalable to both the number of objectives and the size of the model.