GuardReasoner: Towards Reasoning-based LLM Safeguards
Authors: name: Yue Liu, name: Hongcheng Gao, name: Shengfang Zhai, name: Jun Xia, name: Tianyi Wu, name: Zhiwei Xue, name: Yulin Chen, name: Kenji Kawaguchi, name: Jiaheng Zhang, name: Bryan Hooi

As LLMs increasingly impact safety-critical applications, ensuring their
safety using guardrails remains a key challenge. This paper proposes
GuardReasoner, a new safeguard for LLMs, by guiding the guard model to learn to
reason. Concretely, we first create the GuardReasonerTrain dataset, which
consists of 127K samples with 460K detailed reasoning steps. Then, we introduce
reasoning SFT to unlock the reasoning capability of guard models. In addition,
we present hard sample DPO to further strengthen their reasoning ability. In
this manner, GuardReasoner achieves better performance, explainability, and
generalizability. Extensive experiments and analyses on 13 benchmarks of 3
guardrail tasks demonstrate its superiority. Remarkably, GuardReasoner 8B
surpasses GPT-4o+CoT by 5.74% and LLaMA Guard 3 8B by 20.84% F1 score on
average. We release the training data, code, and models with different scales
(1B, 3B, 8B) of GuardReasoner : https://github.com/yueliu1999/GuardReasoner/.