@inproceedings{tu-etal-2025-vilbench,
    title = "{V}i{LB}ench: A Suite for Vision-Language Process Reward Modeling",
    author = "Tu, Haoqin  and
      Feng, Weitao  and
      Chen, Hardy  and
      Liu, Hui  and
      Tang, Xianfeng  and
      Xie, Cihang",
    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.344/",
    pages = "6775--6790",
    ISBN = "979-8-89176-332-6",
    abstract = "Process-supervised reward models serve as a fine-grained function that provides detailed step-wise feedback to model responses, facilitating effective selection of reasoning trajectories for complex tasks. Despite its advantages, evaluation on PRMs remains less explored, especially in the multimodal domain. To address this gap, this paper first benchmarks current vision large language models (VLLMs) as two types of reward models: output reward models (ORMs) and process reward models (PRMs) on multiple vision-language benchmarks, which reveal that neither ORM nor PRM consistently outperforms across all tasks, and superior VLLMs do not necessarily yield better rewarding performance. To further advance evaluation, we introduce ViLBench, a vision-language benchmark designed to require intensive process reward signals. Notably, OpenAI{'}s GPT-4o with Chain-of-Thought (CoT) achieves only 27.3{\%} accuracy, challenging current VLLMs. Lastly, we preliminarily showcase a promising pathway towards bridging the gap between general VLLMs and reward models{---}by collecting 73.6K vision-language process reward data using an enhanced tree-search algorithm, our 3B model is able to achieve an average improvement of 3.3{\%} over standard CoT and up to 2.5{\%} compared to its untrained counterpart on ViLBench by selecting OpenAI o1{'}s generations. We will release our code, model, and data at https://ucsc-vlaa.github.io/ViLBench."
}