GuardReasoner-Omni: A Reasoning-based Multi-modal Guardrail for Text, Image, Video, and Audio
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910265666174976 |
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| author | Zhu, Zhenhao Liu, Yue Guo, Yanpei Qu, Wenjie Chen, Cancan He, Yufei Li, Yibo Chen, Yulin Wu, Tianyi Xu, Huiying Zhu, Xinzhong Zhang, Jiaheng |
| author_facet | Zhu, Zhenhao Liu, Yue Guo, Yanpei Qu, Wenjie Chen, Cancan He, Yufei Li, Yibo Chen, Yulin Wu, Tianyi Xu, Huiying Zhu, Xinzhong Zhang, Jiaheng |
| contents | We present GuardReasoner-Omni, a reasoning-based guardrail model designed to moderate text, image, video, and audio data. First, we construct a comprehensive training corpus comprising 181k samples spanning these four 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 with a concise correctness reward to preserve accurate reasoning while suppressing redundant generation. We release a suite of models scaled at 3B and 7B parameters. Extensive experiments demonstrate that GuardReasoner-Omni achieves superior performance compared to existing state-of-the-art baselines across various guardrail benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03328 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GuardReasoner-Omni: A Reasoning-based Multi-modal Guardrail for Text, Image, Video, and Audio Zhu, Zhenhao Liu, Yue Guo, Yanpei Qu, Wenjie Chen, Cancan He, Yufei Li, Yibo Chen, Yulin Wu, Tianyi Xu, Huiying Zhu, Xinzhong Zhang, Jiaheng Cryptography and Security We present GuardReasoner-Omni, a reasoning-based guardrail model designed to moderate text, image, video, and audio data. First, we construct a comprehensive training corpus comprising 181k samples spanning these four 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 with a concise correctness reward to preserve accurate reasoning while suppressing redundant generation. We release a suite of models scaled at 3B and 7B parameters. Extensive experiments demonstrate that GuardReasoner-Omni achieves superior performance compared to existing state-of-the-art baselines across various guardrail benchmarks. |
| title | GuardReasoner-Omni: A Reasoning-based Multi-modal Guardrail for Text, Image, Video, and Audio |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2602.03328 |