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.