VILA: On Pre-training for Visual Language Models

TL;DR

This paper systematically studies visual language model pre-training, revealing key design choices that improve performance, and introduces VILA, a new model that outperforms existing state-of-the-art models across benchmarks.

Contribution

It provides an in-depth analysis of VLM pre-training strategies and proposes an effective recipe leading to the development of VILA, a superior visual language model.

Findings

Freezing LLMs during pre-training reduces in-context learning.

Interleaved data improves pre-training effectiveness.

Blending text-only and image-text data enhances task performance.

Abstract

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.