LLaVA-o1: Let Vision Language Models Reason Step-by-Step
Authors: name: Guowei Xu; name: Peng Jin; name: Li Hao; name: Yibing Song; name: Lichao Sun; name: Li Yuan
Published: 2024-11-15 18:58:31

Abstract: Large language models have demonstrated substantial advancements in reasoning
capabilities, particularly through inference-time scaling, as illustrated by
models such as OpenAI's o1. However, current Vision-Language Models (VLMs)
often struggle to perform systematic and structured reasoning, especially when
handling complex visual question-answering tasks. In this work, we introduce
LLaVA-o1, a novel VLM designed to conduct autonomous multistage reasoning.
Unlike chain-of-thought prompting, LLaVA-o1 independently engages in sequential
stages of summarization, visual interpretation, logical reasoning, and
conclusion generation. This structured approach enables LLaVA-o1 to achieve
marked improvements in precision on reasoning-intensive tasks. To accomplish
this, we compile the LLaVA-o1-100k dataset, integrating samples from various
visual question answering sources and providing structured reasoning
annotations. Besides, we propose an inference-time stage-level beam search
method, which enables effective inference-time scaling. Remarkably, with only
100k training samples and a simple yet effective inference time scaling method,
LLaVA-o1 not only outperforms its base model by 8.9% on a wide range of
multimodal reasoning benchmarks, but also surpasses the performance of larger
and even closed-source models, such as Gemini-1.5-pro, GPT-4o-mini, and
Llama-3.2-90B-Vision-Instruct.