@inproceedings{she-etal-2025-r,
    title = "{R}-{PRM}: Reasoning-Driven Process Reward Modeling",
    author = "She, Shuaijie  and
      Liu, Junxiao  and
      Liu, Yifeng  and
      Chen, Jiajun  and
      Huang, Xin  and
      Huang, Shujian",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.679/",
    pages = "13449--13462",
    ISBN = "979-8-89176-332-6",
    abstract = "Process Reward Models (PRMs) have emerged as a promising solution to address the reasoning mistakes of large language models (LLMs). However, existing PRMs typically output evaluation scores directly, limiting both learning efficiency and evaluation accuracy. This limitation is further compounded by the scarcity of annotated data. To address these issues, we propose Reasoning-Driven Process Reward Modeling (R-PRM), which activates inherent reasoning to enhance process-level evaluation. First, we leverage stronger LLMs to generate seed data from limited annotations, effectively activating reasoning capabilities and enabling comprehensive step-by-step evaluation. Second, we explore self-improvement of our PRM through preference optimization, without requiring additional annotated data. Third, we introduce inference time scaling to fully harness our model{'}s reasoning potential. Extensive experiments demonstrate R-PRM{'}s effectiveness: on ProcessBench and PRMBench, it surpasses strong baselines by 13.9 and 8.5 F1 scores. When applied to guide mathematical reasoning, R-PRM achieves consistent accuracy improvements of over 8.6 points across six challenging datasets. Further analysis reveals that R-PRM exhibits more comprehensive evaluation and robust generalization, indicating its broader potential."
}