Visual language models (VLMs) rapidly progressed with the recent success of large language models. There have been growing efforts on visual instruction tuning to extend the LLM with visual inputs, but lacks an in-depth study of the visual language pre-training process, where the model learns to perform joint modeling on both modalities. In this work, we examine the design options for VLM pre-training by augmenting LLM towards VLM through step-by-step controllable comparisons. We introduce three main findings: (1) freezing LLMs during pre-training can achieve decent zero-shot performance, but lack in-context learning capability, which requires unfreezing the LLM; (2) interleaved pre-training data is beneficial whereas image-text pairs alone are not optimal; (3) re-blending text-only instruction data to image-text data during instruction fine-tuning not only remedies the degradation of text-only tasks, but also boosts VLM task accuracy. With an enhanced pre-training recipe we build VILA, a Visual Language model family that consistently outperforms the state-of-the-art models, e.g., LLaVA-1.5, across main benchmarks without bells and whistles. Multi-modal pre-training also helps unveil appealing properties of VILA, including multi-image reasoning, enhanced in-context learning, and better world knowledge.

翻译：视觉语言模型（VLM）随大型语言模型的最新成功而迅速发展。尽管在视觉指令微调方面已有诸多努力以扩展LLM使其支持视觉输入，但对视觉语言预训练过程（即模型学习对两种模态进行联合建模的阶段）仍缺乏深入研究。本研究通过逐步可控的比较实验，探索了通过增强LLM构建VLM的预训练设计选择。我们提出三项主要发现：（1）预训练时冻结LLM可获得可观的零样本性能，但会丧失上下文学习能力，这需要解冻LLM；（2）交错式预训练数据更为有益，而仅使用图像-文本对并非最优方案；（3）在指令微调阶段将纯文本指令数据与图像-文本数据混合重平衡，不仅可弥补纯文本任务的性能退化，还能提升VLM任务准确率。基于优化的预训练方案，我们构建了VILA视觉语言模型家族，该模型无需复杂技巧即能在主流基准测试中持续超越现有最优模型（如LLaVA-1.5）。多模态预训练还揭示了VILA的显著特性，包括多图像推理、增强的上下文学习能力以及更优的世界知识。