Enhancing the Reasoning Ability of Multimodal Large Language Models via
  Mixed Preference Optimization
Authors: name: Weiyun Wang, name: Zhe Chen, name: Wenhai Wang, name: Yue Cao, name: Yangzhou Liu, name: Zhangwei Gao, name: Jinguo Zhu, name: Xizhou Zhu, name: Lewei Lu, name: Yu Qiao, name: Jifeng Dai

Existing open-source multimodal large language models (MLLMs) generally
follow a training process involving pre-training and supervised fine-tuning.
However, these models suffer from distribution shifts, which limit their
multimodal reasoning, particularly in the Chain-of-Thought (CoT) performance.
To address this, we introduce a preference optimization (PO) process to enhance
the multimodal reasoning capabilities of MLLMs. Specifically, (1) on the data
side, we design an automated preference data construction pipeline to create
MMPR, a high-quality, large-scale multimodal reasoning preference dataset. and
(2) on the model side, we explore integrating PO with MLLMs, developing a
simple yet effective method, termed Mixed Preference Optimization (MPO), which
boosts multimodal CoT performance. Our approach demonstrates improved
performance across multiple benchmarks, particularly in multimodal reasoning
tasks. Notably, our model, InternVL2-8B-MPO, achieves an accuracy of 67.0 on
MathVista, outperforming InternVL2-8B by 8.7 points and achieving performance
comparable to the 10x larger InternVL2-76B. We hope this study could inspire
further advancements in MLLMs. Code, data, and model shall be publicly
released.