Let ViT Speak: Generative Language-Image Pre-training

In this paper, we present $\mathbf{\text{Gen}}$erative $\mathbf{\text{L}}$anguage-$\mathbf{\text{I}}$mage $\mathbf{\text{P}}$re-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) $\mathbf{\text{Simplicity}}$: a single transformer jointly models visual and textual tokens; (2) $\mathbf{\text{Scalability}}$: it scales effectively with both data and model size; and (3) $\mathbf{\text{Performance}}$: 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.