Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging

TL;DR

This paper introduces a model merging technique to combine vision and reasoning capabilities from different models, enabling training-free transfer of reasoning from LLMs to VLMs and providing insights into internal mechanisms.

Contribution

It proposes a novel method of merging models across modalities to transfer reasoning abilities without additional training, and analyzes how perception and reasoning are represented internally.

Findings

Merging models transfers reasoning capabilities in a training-free manner.

Perception is mainly encoded in early layers, reasoning in middle-to-late layers.

Post-merging, all layers contribute to reasoning, perception distribution remains stable.

Abstract

Vision-Language Models (VLMs) combine visual perception with the general capabilities, such as reasoning, of Large Language Models (LLMs). However, the mechanisms by which these two abilities can be combined and contribute remain poorly understood. In this work, we explore to compose perception and reasoning through model merging that connects parameters of different models. Unlike previous works that often focus on merging models of the same kind, we propose merging models across modalities, enabling the incorporation of the reasoning capabilities of LLMs into VLMs. Through extensive experiments, we demonstrate that model merging offers a successful pathway to transfer reasoning abilities from LLMs to VLMs in a training-free manner. Moreover, we utilize the merged models to understand the internal mechanism of perception and reasoning and how merging affects it. We find that perception capabilities are predominantly encoded in the early layers of the model, whereas reasoning is largely facilitated by the middle-to-late layers. After merging, we observe that all layers begin to contribute to reasoning, whereas the distribution of perception abilities across layers remains largely unchanged. These observations shed light on the potential of model merging as a tool for multimodal integration and interpretation.