VisualPRM: An Effective Process Reward Model for Multimodal Reasoning
We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM)
with 8B parameters, which improves the reasoning abilities of existing
Multimodal Large Language Models (MLLMs) across different model scales and
families with Best-of-N (BoN) evaluation strategies. Specifically, our model
improves the reasoning performance of three types of MLLMs and four different
model scales. Even when applied to the highly capable InternVL2.5-78B, it
achieves a 5.9-point improvement across seven multimodal reasoning benchmarks.
Experimental results show that our model exhibits superior performance compared
to Outcome Reward Models and Self-Consistency during BoN evaluation. To
facilitate the training of multimodal PRMs, we construct a multimodal process
supervision dataset VisualPRM400K using an automated data pipeline. For the
evaluation of multimodal PRMs, we propose VisualProcessBench, a benchmark with
human-annotated step-wise correctness labels, to measure the abilities of PRMs
to detect erroneous steps in multimodal reasoning tasks. We hope that our work
can inspire more future research and contribute to the development of MLLMs.
Our model, data, and benchmark are released in
https://internvl.github.io/blog/2025-03-13-VisualPRM/.
67d3cbea16d1ecea57ed096c
Weiyun Wang
admin_assigned
2025-03-14T11:13:00.956Z
619507e7b74b6c591f794340
https://cdn-avatars.huggingface.co/v1/production/uploads/619507e7b74b6c591f794340/JbPDoy6Ko1V1-6oJJwFV8.jpeg
Weiyun Wang
user
Weiyun1025
67d3cbea16d1ecea57ed096d
Zhangwei Gao
67d3cbea16d1ecea57ed096e
Lianjie Chen
admin_assigned
2025-03-14T11:13:45.138Z
67640bdb1a42694eaf04f748
/avatars/65cc5f348ed573ef095c4a75b5f3ee2e.svg
Lianjie Chen
user
chenlj22
67d3cbea16d1ecea57ed096f
Zhe Chen
claimed_verified
2025-04-23T08:38:44.467Z
64119264f0f81eb569e0d569
/avatars/00dccedea51ac7d2f398ca9d65a8e78b.svg
Zhe Chen
user
czczup
67d3cbea16d1ecea57ed0970
Jinguo Zhu
67d3cbea16d1ecea57ed0971
Xiangyu Zhao
claimed_verified
2025-03-19T13:15:54.592Z
6530e62f536dbca918e71c3e
/avatars/efc93bc767e561c6c6d429f65c23382d.svg
Xiangyu Z
user
PhoenixZ
67d3cbea16d1ecea57ed0972
Yangzhou Liu
67d3cbea16d1ecea57ed0973
Yue Cao
claimed_verified
2025-08-29T11:09:48.461Z
6571382c7644d1128561cebe
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/bnJ-T3k_w1g1Mr7b7LglK.jpeg
Cao Yue
user
yuecao0119
67d3cbea16d1ecea57ed0974
Shenglong Ye
admin_assigned
2025-03-14T11:14:16.758Z
64804866c7f87934d082bb25
/avatars/41761226c79ac16e48d4c4cb84362adb.svg
Yeshenglong
user
Yeshenglong
67d3cbea16d1ecea57ed0975
Xizhou Zhu
admin_assigned
2025-03-14T11:14:23.213Z
64ae2359179421d320b1694b
/avatars/c387a75191005bcaa473091de5383a10.svg
Xizhou Zhu
user
Einsiedler
67d3cbea16d1ecea57ed0976
Lewei Lu
admin_assigned
2025-03-14T11:14:29.188Z
65ead3ea908526a39082e641
/avatars/dcf870695fd56b06ca03d82f831e9019.svg
Lewei Lu
user
luotto
67d3cbea16d1ecea57ed0977
Haodong Duan
admin_assigned
2025-03-14T11:14:35.014Z
63ee1379190ddd6214efd73a
https://cdn-avatars.huggingface.co/v1/production/uploads/1676546883247-noauth.png
HAODONG DUAN
user
KennyUTC
67d3cbea16d1ecea57ed0978
Yu Qiao
67d3cbea16d1ecea57ed0979
Jifeng Dai
admin_assigned
2025-03-14T11:14:54.752Z
64686f7172d9180d4ac8b4e4
/avatars/db67dd6c4b2b41054ddcce5a18ade6f8.svg
Jifeng Dai
user
daijifeng
67d3cbea16d1ecea57ed097a
Wenhai Wang
admin_assigned
2025-03-14T11:14:48.768Z
64d1c560c0c627dfa71bdbe0
/avatars/f42794fe25bffcd870a1bcee69b95298.svg
wenhai.wang
user
wangwhcore
VisualPRM: An Effective Process Reward Model for Multimodal Reasoning
We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM)
with 8B parameters, which improves the reasoning abilities of existing
Multimodal Large Language Models (MLLMs) across different model scales and
families with Best-of-N (BoN) evaluation strategies. Specifically, our model
improves the reasoning performance of three types of MLLMs and four different
model scales. Even when applied to the highly capable InternVL2.5-78B, it
achieves a 5.9-point improvement across seven multimodal reasoning benchmarks.
Experimental results show that our model exhibits superior performance compared
to Outcome Reward Models and Self-Consistency during BoN evaluation. To
facilitate the training of multimodal PRMs, we construct a multimodal process
supervision dataset VisualPRM400K using an automated data pipeline. For the
evaluation of multimodal PRMs, we propose VisualProcessBench, a benchmark with
human-annotated step-wise correctness labels, to measure the abilities of PRMs
to detect erroneous steps in multimodal reasoning tasks. We hope that our work
can inspire more future research and contribute to the development of MLLMs.
Our model, data, and benchmark are released in
https://internvl.github.io/blog/2025-03-13-VisualPRM/.
VisualPRM enhances multimodal reasoning capabilities in MLLMs using Best-of-N strategies and a process supervision dataset, surpassing other models in benchmarks.
Multimodal Process Reward Model Multimodal Large Language Models Best-of-N InternVL2.5-78B multimodal reasoning benchmarks Outcome Reward Models Self-Consistency multimodal process supervision dataset VisualPRM400K VisualProcessBench step-wise correctness labels multimodal reasoning tasks