MM-PRM: Enhancing Multimodal Mathematical Reasoning with Scalable Step-Level Supervision

Lingxiao Du, fanqing meng, kkkai (SII), Zhixiang Zhou, Ping Luo, Qiaosheng ZHANG, SII - Wenqi Shao

MM-PRM: Enhancing Multimodal Mathematical Reasoning with Scalable Step-Level Supervision: 26 upvotes on Hugging Face Daily Papers, #12 of 43 papers on 2025-05-20. Day-by-day upvote history.

While Multimodal Large Language Models (MLLMs) have achieved impressive progress in vision-language understanding, they still struggle with complex multi-step reasoning, often producing logically inconsistent or partially correct solutions. A key limitation lies in the lack of fine-grained supervision over intermediate reasoning steps. To address this, we propose MM-PRM, a process reward model trained within a fully automated, scalable framework. We first build MM-Policy, a strong multimodal model trained on diverse mathematical reasoning data. Then, we construct MM-K12, a curated dataset of 10,000 multimodal math problems with verifiable answers, which serves as seed data. Leveraging a Monte Carlo Tree Search (MCTS)-based pipeline, we generate over 700k step-level annotations without human labeling. The resulting PRM is used to score candidate reasoning paths in the Best-of-N inference setup and achieves significant improvements across both in-domain (MM-K12 test set) and out-of-domain (OlympiadBench, MathVista, etc.) benchmarks. Further analysis confirms the effectiveness of soft labels, smaller learning rates, and path diversity in optimizing PRM performance. MM-PRM demonstrates that process supervision is a powerful tool for enhancing the logical robustness of multimodal reasoning systems. We release all our codes and data at https://github.com/ModalMinds/MM-PRM.

Paper page on Hugging Face · arXiv

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