How Should We Enhance the Safety of Large Reasoning Models: An Empirical
  Study
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.
682e8b3de3d1137730d0517b
Zhexin Zhang
682e8b3de3d1137730d0517c
Xian Qi Loye
claimed_verified
2025-05-27T07:55:24.687Z
6438cb6d7de34e8ea7e5c354
/avatars/36c89dd2762735ccb546290f0dc7e97e.svg
Loye Xian Qi
user
macoloye
682e8b3de3d1137730d0517d
Victor Shea-Jay Huang
682e8b3de3d1137730d0517e
Junxiao Yang
claimed_verified
2025-05-22T07:16:24.874Z
65d859a3661492b25c46a117
/avatars/06a547a00b472f512a25eef4ddd047bf.svg
Junxiao Yang
user
yangjunxiao2021
682e8b3de3d1137730d0517f
Qi Zhu
682e8b3de3d1137730d05180
Shiyao Cui
682e8b3de3d1137730d05181
Fei Mi
682e8b3de3d1137730d05182
Lifeng Shang
682e8b3de3d1137730d05183
Yingkang Wang
682e8b3de3d1137730d05184
Hongning Wang
682e8b3de3d1137730d05185
Minlie Huang
How Should We Enhance the Safety of Large Reasoning Models: An Empirical
  Study
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.
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.
Large Reasoning Models LRMs Supervised Fine-Tuning SFT DeepSeek-R1 data distillation reasoning process safety improvements over-refusal math reasoning