Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision
  Language Models
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.
67b8aaa5ef55d96f2cbd7eaa
Michihiro Yasunaga
claimed_verified
2025-02-21T16:39:03.035Z
621e9388345a1d9ab65391c3
/avatars/d009c811dc63224f8eba1889fefad04b.svg
Michihiro Yasunaga
user
michiyasunaga
67b8aaa5ef55d96f2cbd7eab
Luke Zettlemoyer
67b8aaa5ef55d96f2cbd7eac
Marjan Ghazvininejad
claimed_verified
2025-02-24T09:20:41.579Z
660f0fd377a1e2509aa5a679
/avatars/e04ef05bed0bf6cefdc7e3e39674e2f9.svg
Marjan Ghazvininejad
user
mghazvininejad
Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision
  Language Models
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.
Multimodal RewardBench is an expert-annotated benchmark covering six domains for evaluating multimodal reward models in vision-language models, revealing significant challenges in reasoning and safety.
reward models vision-language models (VLMs) multimodal reward models expert-annotated benchmark general correctness preference knowledge reasoning safety visual question-answering prompt chosen response rejected response VLM judges Gemini 1.5 Pro Claude 3.5 Sonnet