GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning
Authors: name: Yue Liu, name: Shengfang Zhai, name: Mingzhe Du, name: Yulin Chen, name: Tri Cao, name: Hongcheng Gao, name: Cheng Wang, name: Xinfeng Li, name: Kun Wang, name: Junfeng Fang, name: Jiaheng Zhang, name: Bryan Hooi

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/