MM-PRM: Enhancing Multimodal Mathematical Reasoning with Scalable Step-Level Supervision
Lingxiao Du, Fanqing Meng, Zongkai Liu, Zhixiang Zhou, Ping Luo, Qiaosheng Zhang, Wenqi Shao
2025-05-20
Summary
This paper talks about MM-PRM, a new method that helps AI models get better at solving math problems that involve both words and pictures by teaching them step-by-step how to reason through each part of a problem.
What's the problem?
The problem is that current AI models often struggle with complex math questions, especially when they have to understand both text and images at the same time, because they don't always get enough detailed guidance on how to solve each step.
What's the solution?
To solve this, the researchers created a process reward model that gives the AI feedback at every step of the problem, using automated tools to make the training process bigger and more effective. This helps the AI learn how to reason more logically and accurately.
Why it matters?
This matters because it makes AI much better at handling real-world math problems, which often include both words and visuals, and it can help students, teachers, and scientists by providing smarter and more reliable math solutions.
Abstract
MM-PRM, a process reward model with step-level annotations, enhances logical reasoning in multimodal language models by using automated supervision and achieves improved performance on various benchmarks.