Large Reasoning Models (LRMs), such as OpenAI o1 and DeepSeek-R1, have been rapidly progressing and achieving breakthrough performance on complex reasoning tasks such as mathematics and coding. However, the open-source R1 models have raised safety concerns in wide applications, such as the tendency to comply with malicious queries, which greatly impacts the utility of these powerful models in their applications. In this paper, we introduce RealSafe-R1 as safety-aligned versions of DeepSeek-R1 distilled models. To train these models, we construct a dataset of 15k safety-aware reasoning trajectories generated by DeepSeek-R1, under explicit instructions for expected refusal behavior. Both quantitative experiments and qualitative case studies demonstrate the models' improvements, which are shown in their safety guardrails against both harmful queries and jailbreak attacks. Importantly, unlike prior safety alignment efforts that often compromise reasoning performance, our method preserves the models' reasoning capabilities by maintaining the training data within the original distribution of generation. Model weights of RealSafe-R1 are open-source at https://huggingface.co/RealSafe.

本研究解决了大型推理模型在应用中存在的安全隐患，尤其是对恶意查询的高风险反应。提出的RealSafe-R1模型通过构建包含15000条安全意识推理轨迹的数据集，确保了模型在安全性和推理能力之间的平衡。研究结果表明，该模型在抵御有害查询和监狱攻击方面表现优越，为推理模型的安全应用提供了新的思路。

RealSafe-R1：不妨碍推理能力的安全对齐深度搜索R1