title: Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision
    Language Models
summary: Reward models play an essential role in training vision-language models
  (VLMs) by assessing output quality to enable aligning with human preferences.
  Despite their importance, the research community lacks comprehensive open
  benchmarks for evaluating multimodal reward models in VLMs. To address this
  gap, we introduce Multimodal RewardBench, an expert-annotated benchmark
  covering six domains: general correctness, preference, knowledge, reasoning,
  safety, and visual question-answering. Our dataset comprises 5,211 annotated
  (prompt, chosen response, rejected response) triplets collected from various
  VLMs. In evaluating a range of VLM judges, we find that even the top-performing
  models, Gemini 1.5 Pro and Claude 3.5 Sonnet, achieve only 72% overall
  accuracy. Notably, most models struggle in the reasoning and safety domains.
  These findings suggest that Multimodal RewardBench offers a challenging testbed
  for advancing reward model development across multiple domains. We release the
  benchmark at https://github.com/facebookresearch/multimodal_rewardbench.
paper_authors._id: 67b8aaa5ef55d96f2cbd7eaa
paper_authors.name: Michihiro Yasunaga
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T16:39:03.035Z
paper_authors._id: 621e9388345a1d9ab65391c3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/621e9388345a1d9ab65391c3/RxurNzyAWJOUdgeSHQi1R.jpeg
paper_authors.fullname: Michihiro Yasunaga
paper_authors.type: user
paper_authors.user: michiyasunaga
paper_authors._id: 67b8aaa5ef55d96f2cbd7eab
paper_authors.name: Luke Zettlemoyer
paper_authors._id: 67b8aaa5ef55d96f2cbd7eac
paper_authors.name: Marjan Ghazvininejad
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-24T09:20:41.579Z
paper_authors._id: 660f0fd377a1e2509aa5a679
paper_authors.avatarUrl: /avatars/e04ef05bed0bf6cefdc7e3e39674e2f9.svg
paper_authors.fullname: Marjan Ghazvininejad
paper_authors.type: user
paper_authors.user: mghazvininejad
paper_title: Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision
    Language Models
paper_summary: Reward models play an essential role in training vision-language models
  (VLMs) by assessing output quality to enable aligning with human preferences.
  Despite their importance, the research community lacks comprehensive open
  benchmarks for evaluating multimodal reward models in VLMs. To address this
  gap, we introduce Multimodal RewardBench, an expert-annotated benchmark
  covering six domains: general correctness, preference, knowledge, reasoning,
  safety, and visual question-answering. Our dataset comprises 5,211 annotated
  (prompt, chosen response, rejected response) triplets collected from various
  VLMs. In evaluating a range of VLM judges, we find that even the top-performing
  models, Gemini 1.5 Pro and Claude 3.5 Sonnet, achieve only 72% overall
  accuracy. Notably, most models struggle in the reasoning and safety domains.
  These findings suggest that Multimodal RewardBench offers a challenging testbed
  for advancing reward model development across multiple domains. We release the
  benchmark at https://github.com/facebookresearch/multimodal_rewardbench.