title: GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning
summary: 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/
paper_authors._id: 682af4241286a7273c5bfd09
paper_authors.name: Yue Liu
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paper_authors.name: Shengfang Zhai
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paper_authors.name: Mingzhe Du
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paper_authors.fullname: Mingzhe Du
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paper_authors.name: Yulin Chen
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paper_authors.fullname: Yulin Chen
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paper_authors.user: CallMeChen
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paper_authors.name: Tri Cao
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paper_authors.name: Hongcheng Gao
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paper_authors.fullname: Hongcheng Gao
paper_authors.type: user
paper_authors.user: HongchengGao
paper_authors._id: 682af4241286a7273c5bfd0f
paper_authors.name: Cheng Wang
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65c1cbac88462619b2e191aa/aDw9ifBTdeGHdi1O75R9O.jpeg
paper_authors.fullname: Cheng Wang
paper_authors.type: user
paper_authors.user: LLucass
paper_authors._id: 682af4241286a7273c5bfd10
paper_authors.name: Xinfeng Li
paper_authors._id: 682af4241286a7273c5bfd11
paper_authors.name: Kun Wang
paper_authors._id: 682af4241286a7273c5bfd12
paper_authors.name: Junfeng Fang
paper_authors._id: 682af4241286a7273c5bfd13
paper_authors.name: Jiaheng Zhang
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paper_authors.fullname: Jiaheng Zhang
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paper_authors.name: Bryan Hooi
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paper_title: GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning
paper_summary: 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/
paper_ai_summary: 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.
paper_ai_keywords: 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