Autoregressive Semantic Visual Reconstruction Helps VLMs Understand
  Better
Authors: name: Dianyi Wang, name: Wei Song, name: Yikun Wang, name: Siyuan Wang, name: Kaicheng Yu, name: Zhongyu Wei, name: Jiaqi Wang

Typical large vision-language models (LVLMs) apply autoregressive supervision
solely to textual sequences, without fully incorporating the visual modality
into the learning process. This results in three key limitations: (1) an
inability to utilize images without accompanying captions, (2) the risk that
captions omit critical visual details, and (3) the challenge that certain
vision-centric content cannot be adequately conveyed through text. As a result,
current LVLMs often prioritize vision-to-language alignment while potentially
overlooking fine-grained visual information. While some prior works have
explored autoregressive image generation, effectively leveraging autoregressive
visual supervision to enhance image understanding remains an open challenge. In
this paper, we introduce Autoregressive Semantic Visual Reconstruction (ASVR),
which enables joint learning of visual and textual modalities within a unified
autoregressive framework. We show that autoregressively reconstructing the raw
visual appearance of images does not enhance and may even impair multimodal
understanding. In contrast, autoregressively reconstructing the semantic
representation of images consistently improves comprehension. Notably, we find
that even when models are given continuous image features as input, they can
effectively reconstruct discrete semantic tokens, resulting in stable and
consistent improvements across a wide range of multimodal understanding
benchmarks. Our approach delivers significant performance gains across varying
data scales (556k-2M) and types of LLM bacbones. Specifically, ASVR improves
LLaVA-1.5 by 5% in average scores across 14 multimodal benchmarks. The code is
available at https://github.com/AlenjandroWang/ASVR.