Improving Vision-language Models with Perception-centric Process Reward Models

Benchmark Model Rank Results
chart-question-answering-on-chartqaQwen2.5-VL (3B) with Perceval#311:1 Accuracy: 86.48
visual-question-answering-vqa-on-blinkQwen2.5-VL (7B) with Perceval#39Accuracy: 54.49
multimodal-reasoning-on-mathvisionQwen2.5-VL (7B) with Perceval#48Accuracy: 30.92
multimodal-reasoning-on-mathvistaQwen2.5-VL (7B) with Perceval#58Accuracy: 72.0
vision-language-understanding-on-mmstarQwen2.5-VL (7B) with Perceval#66Accuracy: 63.8