How Should We Enhance the Safety of Large Reasoning Models: An Empirical
  Study
Authors: name: Zhexin Zhang, name: Xian Qi Loye, name: Victor Shea-Jay Huang, name: Junxiao Yang, name: Qi Zhu, name: Shiyao Cui, name: Fei Mi, name: Lifeng Shang, name: Yingkang Wang, name: Hongning Wang, name: Minlie Huang

Large Reasoning Models (LRMs) have achieved remarkable success on
reasoning-intensive tasks such as mathematics and programming. However, their
enhanced reasoning capabilities do not necessarily translate to improved safety
performance-and in some cases, may even degrade it. This raises an important
research question: how can we enhance the safety of LRMs? In this paper, we
present a comprehensive empirical study on how to enhance the safety of LRMs
through Supervised Fine-Tuning (SFT). Our investigation begins with an
unexpected observation: directly distilling safe responses from DeepSeek-R1
fails to significantly enhance safety. We analyze this phenomenon and identify
three key failure patterns that contribute to it. We then demonstrate that
explicitly addressing these issues during the data distillation process can
lead to substantial safety improvements. Next, we explore whether a long and
complex reasoning process is necessary for achieving safety. Interestingly, we
find that simply using short or template-based reasoning process can attain
comparable safety performance-and are significantly easier for models to learn
than more intricate reasoning chains. These findings prompt a deeper reflection
on the role of reasoning in ensuring safety. Finally, we find that mixing math
reasoning data during safety fine-tuning is helpful to balance safety and
over-refusal. Overall, we hope our empirical study could provide a more
holistic picture on enhancing the safety of LRMs. The code and data used in our
experiments are released in https://github.com/thu-coai/LRM-Safety-Study.