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

Zhenhao Zhu, Yue Liu, Yanpei Guo, Wenjie Qu, Cancan Chen, Yufei He, Yibo Li, Yulin Chen, Tianyi Wu, Huiying Xu, Xinzhong Zhu, Jiaheng Zhang

Keywords: Multimodal Large Language Models, Guardrail Models, Reasoning Models, Large-scale Reinforcement Learning

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.

Submission Number: 90