GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning
To enhance the safety of VLMs, this paper introduces a novel reasoning-based
VLM guard model dubbed GuardReasoner-VL. The core idea is to incentivize the
guard model to deliberatively reason before making moderation decisions via
online RL. First, we construct GuardReasoner-VLTrain, a reasoning corpus with
123K samples and 631K reasoning steps, spanning text, image, and text-image
inputs. Then, based on it, we cold-start our model's reasoning ability via SFT.
In addition, we further enhance reasoning regarding moderation through online
RL. Concretely, to enhance diversity and difficulty of samples, we conduct
rejection sampling followed by data augmentation via the proposed safety-aware
data concatenation. Besides, we use a dynamic clipping parameter to encourage
exploration in early stages and exploitation in later stages. To balance
performance and token efficiency, we design a length-aware safety reward that
integrates accuracy, format, and token cost. Extensive experiments demonstrate
the superiority of our model. Remarkably, it surpasses the runner-up by 19.27%
F1 score on average. We release data, code, and models (3B/7B) of
GuardReasoner-VL at https://github.com/yueliu1999/GuardReasoner-VL/
682af4241286a7273c5bfd09
Yue Liu
admin_assigned
2025-05-19T14:18:09.117Z
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yueliu1999
user
yueliu1999
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Shengfang Zhai
admin_assigned
2025-05-19T14:18:16.514Z
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Shengfang Zhai
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zsf
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Mingzhe Du
admin_assigned
2025-05-19T14:18:22.634Z
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Mingzhe Du
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Elfsong
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Yulin Chen
admin_assigned
2025-05-19T14:18:29.818Z
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Yulin Chen
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CallMeChen
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Tri Cao
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Hongcheng Gao
admin_assigned
2025-05-19T14:18:48.924Z
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Hongcheng Gao
user
HongchengGao
682af4241286a7273c5bfd0f
Cheng Wang
claimed_verified
2025-05-22T07:18:17.497Z
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Cheng Wang
user
LLucass
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Xinfeng Li
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Kun Wang
682af4241286a7273c5bfd12
Junfeng Fang
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Jiaheng Zhang
admin_assigned
2025-05-19T14:19:28.414Z
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Jiaheng Zhang
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jiaheng233
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Bryan Hooi
admin_assigned
2025-05-19T14:19:35.772Z
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Bryan Hooi
user
bhooi
GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning
To enhance the safety of VLMs, this paper introduces a novel reasoning-based
VLM guard model dubbed GuardReasoner-VL. The core idea is to incentivize the
guard model to deliberatively reason before making moderation decisions via
online RL. First, we construct GuardReasoner-VLTrain, a reasoning corpus with
123K samples and 631K reasoning steps, spanning text, image, and text-image
inputs. Then, based on it, we cold-start our model's reasoning ability via SFT.
In addition, we further enhance reasoning regarding moderation through online
RL. Concretely, to enhance diversity and difficulty of samples, we conduct
rejection sampling followed by data augmentation via the proposed safety-aware
data concatenation. Besides, we use a dynamic clipping parameter to encourage
exploration in early stages and exploitation in later stages. To balance
performance and token efficiency, we design a length-aware safety reward that
integrates accuracy, format, and token cost. Extensive experiments demonstrate
the superiority of our model. Remarkably, it surpasses the runner-up by 19.27%
F1 score on average. We release data, code, and models (3B/7B) of
GuardReasoner-VL at https://github.com/yueliu1999/GuardReasoner-VL/
GuardReasoner-VL enhances VLM safety through a reasoning-based guard model trained with SFT and online RL, using diverse datasets and a length-aware safety reward.
GuardReasoner-VL reasoning-based VLM guard model online RL SFT GuardReasoner-VLTrain rejection sampling safety-aware data concatenation dynamic clipping parameter length-aware safety reward F1 score