Towards Understanding Multimodal Fine-Tuning: Spatial Features

TL;DR

This paper introduces a novel analysis method to understand how vision-language models adapt during multimodal fine-tuning, revealing the emergence of spatial features and their neural basis within the model.

Contribution

It presents the first mechanistic analysis of VLM adaptation using stage-wise model diffing, identifying how spatial features and visual grounding develop in language models.

Findings

Spatial features emerge during fine-tuning.

A small group of attention heads activate spatial features.

Visual grounding reshapes previously text-only features.

Abstract

Contemporary Vision-Language Models (VLMs) achieve strong performance on a wide range of tasks by pairing a vision encoder with a pre-trained language model, fine-tuned for visual-text inputs. Yet despite these gains, it remains unclear how language backbone representations adapt during multimodal training and when vision-specific capabilities emerge. In this work, we present the first mechanistic analysis of VLM adaptation. Using stage-wise model diffing, a technique that isolates representational changes introduced during multimodal fine-tuning, we reveal how a language model learns to "see". We first identify vision-preferring features that emerge or reorient during fine-tuning. We then show that a selective subset of these features reliably encodes spatial relations, revealed through controlled shifts to spatial prompts. Finally, we trace the causal activation of these features to a small group of attention heads. Our findings show that stage-wise model diffing reveals when and where spatially grounded multimodal features arise. It also provides a clearer view of modality fusion by showing how visual grounding reshapes features that were previously text-only. This methodology enhances the interpretability of multimodal training and provides a foundation for understanding and refining how pretrained language models acquire vision-grounded capabilities.