| title | Let ViT Speak: Generative Language-Image Pre-training |
|---|---|
| authors | Yan Fang, Mengcheng Lan, Zilong Huang, Weixian Lei, Yunqing Zhao, Yujie Zhong, Yingchen Yu, Qi She, Yao Zhao, Yunchao Wei |
| date | 2026-05-01 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.00809 |
| affiliation | ByteDance |
| upvotes | 32 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | In this paper, we present Generative Language-Image Pre-training (GenLIP), a minimalist generative pretraining framework for Vision Transformers (ViTs) designed for multimodal large language models (MLLMs). To better align vision encoders with the autoregressive nature of LLMs, GenLIP trains a ViT to predict language tokens directly from visual tokens using a standard language modeling objective, without contrastive batch construction or an additional text decoder. This design offers three key advantages: (1) Simplicity: a single transformer jointly models visual and textual tokens; (2) Scalability: it scales effectively with both data and model size; and (3) Performance: it achieves competitive or superior results across diverse multimodal benchmarks. Trained on 8B samples from Recap-DataComp-1B, GenLIP matches or surpasses strong baselines despite using substantially less pretraining data. After continued pretraining on multi-resolution images at native aspect ratios, GenLIP further improves on detail-sensitive tasks such as OCR and chart understanding, making it a strong foundation for vision encoders in MLLMs. |
cite
@misc{fang2026let,
title={Let ViT Speak: Generative Language-Image Pre-training},
author={Yan Fang and Mengcheng Lan and Zilong Huang and Weixian Lei and Yunqing Zhao and Yujie Zhong and Yingchen Yu and Qi She and Yao Zhao and Yunchao Wei},
year={2026},
eprint={2605.00809},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.00809}
}