title: VLRewardBench: A Challenging Benchmark for Vision-Language Generative
    Reward Models
summary: Vision-language generative reward models (VL-GenRMs) play a crucial role in
  aligning and evaluating multimodal AI systems, yet their own evaluation remains
  under-explored. Current assessment methods primarily rely on AI-annotated
  preference labels from traditional VL tasks, which can introduce biases and
  often fail to effectively challenge state-of-the-art models. To address these
  limitations, we introduce VL-RewardBench, a comprehensive benchmark spanning
  general multimodal queries, visual hallucination detection, and complex
  reasoning tasks. Through our AI-assisted annotation pipeline combining sample
  selection with human verification, we curate 1,250 high-quality examples
  specifically designed to probe model limitations. Comprehensive evaluation
  across 16 leading large vision-language models, demonstrates VL-RewardBench's
  effectiveness as a challenging testbed, where even GPT-4o achieves only 65.4%
  accuracy, and state-of-the-art open-source models such as Qwen2-VL-72B,
  struggle to surpass random-guessing. Importantly, performance on VL-RewardBench
  strongly correlates (Pearson's r > 0.9) with MMMU-Pro accuracy using Best-of-N
  sampling with VL-GenRMs. Analysis experiments uncover three critical insights
  for improving VL-GenRMs: (i) models predominantly fail at basic visual
  perception tasks rather than reasoning tasks; (ii) inference-time scaling
  benefits vary dramatically by model capacity; and (iii) training VL-GenRMs to
  learn to judge substantially boosts judgment capability (+14.7% accuracy for a
  7B VL-GenRM). We believe VL-RewardBench along with the experimental insights
  will become a valuable resource for advancing VL-GenRMs.
paper_authors._id: 6746f9c205afcd8837d520b7
paper_authors.name: Lei Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-09T14:47:54.952Z
paper_authors._id: 6038d6d0612f5eef3cc05ea9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6038d6d0612f5eef3cc05ea9/ryhvAX5djQpD5OrIlZQ1f.jpeg
paper_authors.fullname: Lei Li
paper_authors.type: user
paper_authors.user: tobiaslee
paper_authors._id: 6746f9c205afcd8837d520b8
paper_authors.name: Yuancheng Wei
paper_authors._id: 6746f9c205afcd8837d520b9
paper_authors.name: Zhihui Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-28T12:19:47.072Z
paper_authors._id: 622f103fc78da4c7ebd7c887
paper_authors.avatarUrl: /avatars/b0c7cd29835d92c2cd584947fcd5d520.svg
paper_authors.fullname: Xie
paper_authors.type: user
paper_authors.user: Zhihui
paper_authors._id: 6746f9c205afcd8837d520ba
paper_authors.name: Xuqing Yang
paper_authors._id: 6746f9c205afcd8837d520bb
paper_authors.name: Yifan Song
paper_authors._id: 6746f9c205afcd8837d520bc
paper_authors.name: Peiyi Wang
paper_authors._id: 6746f9c205afcd8837d520bd
paper_authors.name: Chenxin An
paper_authors._id: 6746f9c205afcd8837d520be
paper_authors.name: Tianyu Liu
paper_authors._id: 6746f9c205afcd8837d520bf
paper_authors.name: Sujian Li
paper_authors._id: 6746f9c205afcd8837d520c0
paper_authors.name: Bill Yuchen Lin
paper_authors._id: 6746f9c205afcd8837d520c1
paper_authors.name: Lingpeng Kong
paper_authors._id: 6746f9c205afcd8837d520c2
paper_authors.name: Qi Liu
paper_title: VLRewardBench: A Challenging Benchmark for Vision-Language Generative
    Reward Models
paper_summary: Vision-language generative reward models (VL-GenRMs) play a crucial role in
  aligning and evaluating multimodal AI systems, yet their own evaluation remains
  under-explored. Current assessment methods primarily rely on AI-annotated
  preference labels from traditional VL tasks, which can introduce biases and
  often fail to effectively challenge state-of-the-art models. To address these
  limitations, we introduce VL-RewardBench, a comprehensive benchmark spanning
  general multimodal queries, visual hallucination detection, and complex
  reasoning tasks. Through our AI-assisted annotation pipeline combining sample
  selection with human verification, we curate 1,250 high-quality examples
  specifically designed to probe model limitations. Comprehensive evaluation
  across 16 leading large vision-language models, demonstrates VL-RewardBench's
  effectiveness as a challenging testbed, where even GPT-4o achieves only 65.4%
  accuracy, and state-of-the-art open-source models such as Qwen2-VL-72B,
  struggle to surpass random-guessing. Importantly, performance on VL-RewardBench
  strongly correlates (Pearson's r > 0.9) with MMMU-Pro accuracy using Best-of-N
  sampling with VL-GenRMs. Analysis experiments uncover three critical insights
  for improving VL-GenRMs: (i) models predominantly fail at basic visual
  perception tasks rather than reasoning tasks; (ii) inference-time scaling
  benefits vary dramatically by model capacity; and (iii) training VL-GenRMs to
  learn to judge substantially boosts judgment capability (+14.7% accuracy for a
  7B VL-GenRM). We believe VL-RewardBench along with the experimental insights
  will become a valuable resource for advancing VL-GenRMs.
paper_ai_keywords: vision-language generative reward models (VL-GenRMs) multimodal AI systems AI-annotated preference labels VL tasks VL-RewardBench multimodal queries visual hallucination detection complex reasoning tasks CL-assisted annotation pipeline sample selection human verification evaluation leading large vision-language models caching testbed GPT-4o Qwen2-VL-72B random-guessing MMMU-Pro accuracy Best-of-N sampling visual perception tasks reasoning tasks inference-time scaling model capacity judgment capability training VL-GenRMs