title: How Should We Enhance the Safety of Large Reasoning Models: An Empirical
    Study
summary: 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.
paper_authors._id: 682e8b3de3d1137730d0517b
paper_authors.name: Zhexin Zhang
paper_authors._id: 682e8b3de3d1137730d0517c
paper_authors.name: Xian Qi Loye
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:55:24.687Z
paper_authors._id: 6438cb6d7de34e8ea7e5c354
paper_authors.avatarUrl: /avatars/36c89dd2762735ccb546290f0dc7e97e.svg
paper_authors.fullname: Loye Xian Qi
paper_authors.type: user
paper_authors.user: macoloye
paper_authors._id: 682e8b3de3d1137730d0517d
paper_authors.name: Victor Shea-Jay Huang
paper_authors._id: 682e8b3de3d1137730d0517e
paper_authors.name: Junxiao Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-22T07:16:24.874Z
paper_authors._id: 65d859a3661492b25c46a117
paper_authors.avatarUrl: /avatars/06a547a00b472f512a25eef4ddd047bf.svg
paper_authors.fullname: junxiao yang
paper_authors.type: user
paper_authors.user: yangjunxiao2021
paper_authors._id: 682e8b3de3d1137730d0517f
paper_authors.name: Qi Zhu
paper_authors._id: 682e8b3de3d1137730d05180
paper_authors.name: Shiyao Cui
paper_authors._id: 682e8b3de3d1137730d05181
paper_authors.name: Fei Mi
paper_authors._id: 682e8b3de3d1137730d05182
paper_authors.name: Lifeng Shang
paper_authors._id: 682e8b3de3d1137730d05183
paper_authors.name: Yingkang Wang
paper_authors._id: 682e8b3de3d1137730d05184
paper_authors.name: Hongning Wang
paper_authors._id: 682e8b3de3d1137730d05185
paper_authors.name: Minlie Huang
paper_title: How Should We Enhance the Safety of Large Reasoning Models: An Empirical
    Study
paper_summary: 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.
paper_ai_summary: The study investigates methods to enhance the safety of Large Reasoning Models (LRMs) through Supervised Fine-Tuning (SFT), finding that explicit addressing of failure patterns and use of simpler reasoning processes can improve safety without requiring complex reasoning chains or excessive data.
paper_ai_keywords: Large Reasoning Models LRMs Supervised Fine-Tuning SFT DeepSeek-R1 data distillation reasoning process safety improvements over-refusal math reasoning