@inproceedings{wang-etal-2025-reasoning,
    title = "Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills",
    author = "Wang, Changsheng  and
      Fan, Chongyu  and
      Zhang, Yihua  and
      Jia, Jinghan  and
      Wei, Dennis  and
      Ram, Parikshit  and
      Baracaldo, Nathalie  and
      Liu, Sijia",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.220/",
    pages = "4427--4443",
    ISBN = "979-8-89176-332-6",
    abstract = "Recent advances in large reasoning models (LRMs) have enabled strong multi-step reasoning capabilities. However, existing machine unlearning algorithms are tailored to standard language modeling and fail to address the unique challenges posed by LRMs. In this work, we present the first systematic study of LRM unlearning and reveal that conventional unlearning methods often overlook critical information leakage in reasoning traces, even when final answers are successfully removed. To address this, we propose Reasoning-aware Representation Misdirection for Unlearning ($R^2$MU), a method that suppresses sensitive reasoning traces while preserving the model{'}s general reasoning ability. Our experiments demonstrate that $R^2$MU significantly reduces reasoning trace leakage and achieves strong performance across both reasoning and safety benchmarks, including WMDP, StrongReject, JBB-Behaviors and WildJailbreak, under state-of-the-art models such as DeepSeek-R1-Distill-LLaMA-8B and DeepSeek-R1-Distill-Qwen-14B. To the best of our knowledge, MU is the first principled approach to both expose and mitigate reasoning trace leakage in LRM unlearning, while preserving reasoning ability."
}