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.

翻译：典型的大型视觉语言模型仅对文本序列应用自回归监督，未能将视觉模态充分融入学习过程。这导致三个关键局限：(1) 无法利用无伴随描述的图像，(2) 存在描述遗漏关键视觉细节的风险，(3) 某些以视觉为中心的内容难以通过文本充分传达。因此，现有大型视觉语言模型往往优先考虑视觉-语言对齐，而可能忽略细粒度视觉信息。虽然先前研究已探索自回归图像生成，但如何有效利用自回归视觉监督来增强图像理解仍是开放挑战。本文提出自回归语义视觉重建方法，实现在统一自回归框架中对视觉与文本模态的联合学习。研究表明：自回归重建原始视觉外观不仅无法提升、甚至可能损害多模态理解；而自回归重建图像语义表征则能持续提升理解能力。值得注意的是，即使模型以连续图像特征作为输入，仍能有效重建离散语义标记，从而在广泛的多模态理解基准测试中实现稳定一致的性能提升。该方法在不同数据规模（556k-2M）和各类大语言模型骨干网络中均取得显著性能增益。具体而言，ASVR将LLaVA-1.5在14项多模态基准测试中的平均得分提升5%。代码发布于https://github.com/AlenjandroWang/ASVR。