title: VisualPRM: An Effective Process Reward Model for Multimodal Reasoning
summary: 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/.
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paper_authors.fullname: Xizhou Zhu
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paper_title: VisualPRM: An Effective Process Reward Model for Multimodal Reasoning
paper_summary: 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/.
paper_ai_keywords: Process Reward Model (PRM) Multimodal Large Language Models (MLLMs) Best-of-N (BoN) multimodal reasoning benchmarks Automated data pipeline VisualPRM400K VisualProcessBench human-annotated step-wise correctness labels multimodal PRMs erroneous steps in multimodal reasoning tasks