Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
Authors: name: Ang Li, name: Charles Wang, name: Kaiyu Yue, name: Zikui Cai, name: Ollie Liu, name: Deqing Fu, name: Peng Guo, name: Wang Bill Zhu, name: Vatsal Sharan, name: Robin Jia, name: Willie Neiswanger, name: Furong Huang, name: Tom Goldstein, name: Micah Goldblum
Organization: Zebra CoT

Humans often use visual aids, for example diagrams or sketches, when solving
complex problems. Training multimodal models to do the same, known as Visual
Chain of Thought (Visual CoT), is challenging due to: (1) poor off-the-shelf
visual CoT performance, which hinders reinforcement learning, and (2) the lack
of high-quality visual CoT training data. We introduce Zebra-CoT, a
diverse large-scale dataset with 182,384 samples, containing logically coherent
interleaved text-image reasoning traces. We focus on four categories of tasks
where sketching or visual reasoning is especially natural, spanning scientific
questions such as geometry, physics, and algorithms; 2D visual reasoning tasks
like visual search and jigsaw puzzles; 3D reasoning tasks including 3D
multi-hop inference, embodied and robot planning; visual logic problems and
strategic games like chess. Fine-tuning the Anole-7B model on the Zebra-CoT
training corpus results in an improvement of +12% in our test-set accuracy and
yields up to +13% performance gain on standard VLM benchmark evaluations.
Fine-tuning Bagel-7B yields a model that generates high-quality interleaved
visual reasoning chains, underscoring Zebra-CoT's effectiveness for developing
multimodal reasoning abilities. We open-source our dataset and models to
support development and evaluation of visual CoT.