While open-source vision-language models perform well on simple question-answering, they still struggle with complex questions that require both perceptual and reasoning capabilities. We propose LATTE, a family of vision-language models that have LeArned to Think wiTh vision spEcialists. By offloading perception to state-of-the-art vision models, our approach enables vision-language models to focus solely on reasoning over high-quality perceptual information. To train LATTE, we synthesize and filter a large dataset of 293K multi-modal reasoning traces over perceptual outputs of vision specialists. LATTE trained on this data achieves significant 4-5% gains over baselines across 6 benchmarks covering both perception and reasoning abilities. Ablation studies reveal that the effectiveness of multi-modal reasoning traces depends on the data sources, formats, and quality of thoughts.
@article{arxiv.2412.05479,
title = {LATTE: Learning to Think with Vision Specialists},
author = {Zixian Ma and Jianguo Zhang and Zhiwei Liu and Jieyu Zhang and Juntao Tan and Manli Shu and Juan Carlos Niebles and Shelby Heinecke and Huan Wang and Caiming Xiong and Ranjay Krishna and Silvio Savarese},
journal= {arXiv preprint arXiv:2412.05479},
year = {2025}
}