Don't Blind Your VLA: Aligning Visual Representations for OOD Generalization

TL;DR

This paper investigates how fine-tuning Vision-Language-Action models affects their visual representations, revealing degradation issues and proposing alignment strategies to improve out-of-distribution generalization.

Contribution

It systematically analyzes representation retention during VLA fine-tuning and introduces an effective alignment method to mitigate visual degradation and enhance OOD performance.

Findings

Naive fine-tuning degrades visual representations.

Alignment strategies can recover VL capabilities.

Proposed method improves OOD generalization.

Abstract

The growing success of Vision-Language-Action (VLA) models stems from the promise that pretrained Vision-Language Models (VLMs) can endow agents with transferable world knowledge and vision-language (VL) grounding, laying a foundation for action models with broader generalization. Yet when these VLMs are adapted to the action modality, it remains unclear to what extent their original VL representations and knowledge are preserved. In this work, we conduct a systematic study of representation retention during VLA fine-tuning, showing that naive action fine-tuning leads to degradation of visual representations. To characterize and measure these effects, we probe VLA's hidden representations and analyze attention maps, further, we design a set of targeted tasks and methods that contrast VLA models with their counterpart VLMs, isolating changes in VL capabilities induced by action fine-tuning. We further evaluate a range of strategies for aligning visual representations and introduce a simple yet effective method that mitigates degradation and yields improved generalization to out-of-distribution (OOD) scenarios. Taken together, our analysis clarifies the trade-off between action fine-tuning and the degradation of VL representations and highlights practical approaches to recover inherited VL capabilities. Code is publicly available: https://blind-vla-paper.github.io