ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual
  Perception in VLMs
Reinforcement learning (RL) has shown great effectiveness for fine-tuning
large language models (LLMs) using tasks that are challenging yet easily
verifiable, such as math reasoning or code generation. However, extending this
success to visual perception in vision-language models (VLMs) has been impeded
by the scarcity of vision-centric tasks that are simultaneously challenging and
unambiguously verifiable. To this end, we introduce ViCrit (Visual Caption
Hallucination Critic), an RL proxy task that trains VLMs to localize a subtle,
synthetic visual hallucination injected into paragraphs of human-written image
captions. Starting from a 200-word captions, we inject a single, subtle visual
description error-altering a few words on objects, attributes, counts, or
spatial relations-and task the model to pinpoint the corrupted span given the
image and the modified caption. This formulation preserves the full perceptual
difficulty while providing a binary, exact-match reward that is easy to compute
and unambiguous. Models trained with the ViCrit Task exhibit substantial gains
across a variety of VL benchmarks. Crucially, the improvements transfer beyond
natural-image training data to abstract image reasoning and visual math,
showing promises of learning to perceive rather than barely memorizing seen
objects. To facilitate evaluation, we further introduce ViCrit-Bench, a
category-balanced diagnostic benchmark that systematically probes perception
errors across diverse image domains and error types. Together, our results
demonstrate that fine-grained hallucination criticism is an effective and
generalizable objective for enhancing visual perception in VLMs.
684fed081d9b438aa3957a50
Xiyao Wang
admin_assigned
2025-06-16T13:57:28.955Z
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Xiyao Wang
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russwang
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Zhengyuan Yang
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2025-06-16T13:57:36.188Z
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Zhengyuan Yang
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zyang39
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Chao Feng
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2025-06-23T08:30:06.819Z
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Chao Feng
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chfeng
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Yongyuan Liang
admin_assigned
2025-06-16T13:58:44.352Z
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Yongyuan Liang
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cheryyunl
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Yuhang Zhou
684fed081d9b438aa3957a55
Xiaoyu Liu
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Ziyi Zang
admin_assigned
2025-06-16T13:58:34.481Z
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Ziyi Zang
user
ZYii
684fed081d9b438aa3957a57
Ming Li
684fed081d9b438aa3957a58
Chung-Ching Lin
claimed_verified
2025-07-23T08:37:30.164Z
6669f406d21cf7d39b1d98ba
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CC Lin
user
cclin10
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Kevin Lin
684fed081d9b438aa3957a5a
Linjie Li
684fed081d9b438aa3957a5b
Furong Huang
admin_assigned
2025-06-16T13:58:00.984Z
64cbc3e2a257a3212c00a115
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Furong Huang Lab at UMD
user
furongh-lab
684fed081d9b438aa3957a5c
Lijuan Wang
admin_assigned
2025-06-16T13:57:54.886Z
6413521d4e5305c14f22e110
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Lijuan Wang
user
Lijuan
ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual
  Perception in VLMs
Reinforcement learning (RL) has shown great effectiveness for fine-tuning
large language models (LLMs) using tasks that are challenging yet easily
verifiable, such as math reasoning or code generation. However, extending this
success to visual perception in vision-language models (VLMs) has been impeded
by the scarcity of vision-centric tasks that are simultaneously challenging and
unambiguously verifiable. To this end, we introduce ViCrit (Visual Caption
Hallucination Critic), an RL proxy task that trains VLMs to localize a subtle,
synthetic visual hallucination injected into paragraphs of human-written image
captions. Starting from a 200-word captions, we inject a single, subtle visual
description error-altering a few words on objects, attributes, counts, or
spatial relations-and task the model to pinpoint the corrupted span given the
image and the modified caption. This formulation preserves the full perceptual
difficulty while providing a binary, exact-match reward that is easy to compute
and unambiguous. Models trained with the ViCrit Task exhibit substantial gains
across a variety of VL benchmarks. Crucially, the improvements transfer beyond
natural-image training data to abstract image reasoning and visual math,
showing promises of learning to perceive rather than barely memorizing seen
objects. To facilitate evaluation, we further introduce ViCrit-Bench, a
category-balanced diagnostic benchmark that systematically probes perception
errors across diverse image domains and error types. Together, our results
demonstrate that fine-grained hallucination criticism is an effective and
generalizable objective for enhancing visual perception in VLMs.
ViCrit, an RL task for fine-tuning VLMs, improves visual perception by training models to detect subtle hallucinations in image captions, with gains transferable to various visual domains.
reinforcement learning (RL) large language models (LLMs) vision-language models (VLMs) visual caption hallucination critic perceptual difficulty binary reward exact-match reward ViCrit-Bench abstract image reasoning visual math