GuardReasoner-Omni: A Reasoning-based Multi-modal Guardrail for Text, Image, and Video

• URL: http://arxiv.org/abs/2602.03328v1
• Date: Tue, 03 Feb 2026 09:56:20 GMT
• Title: GuardReasoner-Omni: A Reasoning-based Multi-modal Guardrail for Text, Image, and Video
• Authors: Zhenhao Zhu, Yue Liu, Yanpei Guo, Wenjie Qu, Cancan Chen, Yufei He, Yibo Li, Yulin Chen, Tianyi Wu, Huiying Xu, Xinzhong Zhu, Jiaheng Zhang,
• Abstract summary: GuardReasoner- Omni is a guardrail model designed to moderate text, image, and video data.<n>We construct a comprehensive training corpus comprising 148k samples spanning these three modalities.<n>Our training pipeline follows a two-stage paradigm to incentivize the model to deliberate before making decisions.
• Score: 38.35856368247741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present GuardReasoner-Omni, a reasoning-based guardrail model designed to moderate text, image, and video data. First, we construct a comprehensive training corpus comprising 148k samples spanning these three modalities. Our training pipeline follows a two-stage paradigm to incentivize the model to deliberate before making decisions: (1) conducting SFT to cold-start the model with explicit reasoning capabilities and structural adherence; and (2) performing RL, incorporating an error-driven exploration reward to incentivize deeper reasoning on hard samples. We release a suite of models scaled at 2B and 4B parameters. Extensive experiments demonstrate that GuardReasoner-Omni achieves superior performance compared to existing state-of-the-art baselines across various guardrail benchmarks. Notably, GuardReasoner-Omni (2B) significantly surpasses the runner-up by 5.3% F1 score.

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