Improve Vision Language Model Chain-of-thought Reasoning
Authors: name: Ruohong Zhang, name: Bowen Zhang, name: Yanghao Li, name: Haotian Zhang, name: Zhiqing Sun, name: Zhe Gan, name: Yinfei Yang, name: Ruoming Pang, name: Yiming Yang

Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial
for improving interpretability and trustworthiness. However, current training
recipes lack robust CoT reasoning data, relying on datasets dominated by short
annotations with minimal rationales. In this work, we show that training VLM on
short answers does not generalize well to reasoning tasks that require more
detailed responses. To address this, we propose a two-fold approach. First, we
distill rationales from GPT-4o model to enrich the training data and fine-tune
VLMs, boosting their CoT performance. Second, we apply reinforcement learning
to further calibrate reasoning quality. Specifically, we construct positive
(correct) and negative (incorrect) pairs of model-generated reasoning chains,
by comparing their predictions with annotated short answers. Using this
pairwise data, we apply the Direct Preference Optimization algorithm to refine
the model's reasoning abilities. Our experiments demonstrate significant
improvements in CoT reasoning on benchmark datasets and better generalization
to direct answer prediction as well. This work emphasizes the importance of
incorporating detailed rationales in training and leveraging reinforcement
learning to strengthen the reasoning capabilities of VLMs.