M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data

TL;DR

This paper introduces M-Loss, a new metric to evaluate and improve the compatibility of model merging using limited unlabeled data, bridging the gap between merging and ensembling techniques.

Contribution

The paper proposes M-Loss, a novel evaluation metric that quantifies model merging compatibility and guides effective merging strategies with minimal data.

Findings

M-Loss improves the alignment between merged models and ensembling.

Incorporating M-Loss enhances model merging effectiveness.

The approach is scalable and requires limited unlabeled data.

Abstract

Training of large-scale models is both computationally intensive and often constrained by the availability of labeled data. Model merging offers a compelling alternative by directly integrating the weights of multiple source models without requiring additional data or extensive training. However, conventional model merging techniques, such as parameter averaging, often suffer from the unintended combination of non-generalizable features, especially when source models exhibit significant weight disparities. Comparatively, model ensembling generally provides more stable and superior performance that aggregates multiple models by averaging outputs. However, it incurs higher inference costs and increased storage requirements. While previous studies experimentally showed the similarities between model merging and ensembling, theoretical evidence and evaluation metrics remain lacking. To address this gap, we introduce Merging-ensembling loss (M-Loss), a novel evaluation metric that quantifies the compatibility of merging source models using very limited unlabeled data. By measuring the discrepancy between parameter averaging and model ensembling at layer and node levels, M-Loss facilitates more effective merging strategies. Specifically, M-Loss serves both as a quantitative criterion of the theoretical feasibility of model merging, and a guide for parameter significance in model pruning. Our theoretical analysis and empirical evaluations demonstrate that incorporating M-Loss into the merging process significantly improves the alignment between merged models and model ensembling, providing a scalable and efficient framework for accurate model consolidation.