Elamur Research⌕
☰
home / papers / 2026 / Reason-KE++: Aligning the Process, Not Just the Outcome, for Faithful LLM Knowledge Editing

Reason-KE++: Aligning the Process, Not Just the Outcome, for Faithful LLM Knowledge Editing

Paper · 2026 · Yuchen Wu, Liang Ding, Li Shen et al.

Summary

Multi-hop knowledge editing asks an LLM to reason over newly supplied facts that contradict what it learned in pre-training. The authors argue that the SFT-based state of the art, Reason-KE, mostly learns to copy the output format. As a result, the model's stored associations can quietly win over the edits, causing hallucinated intermediate facts. Reason-KE++ addresses this in two phases. First, a cold-start SFT stage uses reasoning chains produced by GPT-4o-mini from COUNTERFACT multi-hop QA pairs, with automatic and human verification. The model learns to acknowledge edits, decompose the question, and answer sub-questions in sequence. Second, PPO training applies a Stage-aware Reward that scores the intermediate steps as well as the final answer. On MQuAKE-CF-3k, Reason-KE++ reaches 95.48% accuracy and stays robust under distractors. The paper also shows that rewarding only the final answer can raise accuracy while damaging the reasoning itself. Its broader point is that trustworthy editing requires supervising the reasoning process, not just the result.

Key points

  • Reasoning format: inside <think>, the model writes three tagged stages (Acknowledge, Decompose, Act), marks sub-answers with \boxed{}, and gives the final output in <answer> tags.
  • Reward design: the reward combines format validation, a process score (hop, decomposition and sub-answer scores) and an outcome score, weighted as α·process + (1−α)·outcome.
  • Main result: 95.48% multi-hop accuracy on MQuAKE-CF-3k, 5.28 points above Reason-KE. Under severe distractors, performance falls by only 5.06%.
  • Outcome-only RL: rewarding only the final answer drives hop accuracy down to 19.00%, even though final accuracy appears to improve.
  • Constrained decoding: replacing the format reward with strict constrained decoding lowers results by 13.43% on average. In a case study, Reason-KE wrongly names Houston as NASA's headquarters, while Reason-KE++ correctly gives Washington D.C.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Shanghai Jiao Tong UniversityShanghai · China212 works · 19,544 citations🎓Nanyang Technological UniversitySingapore · Singapore184 works · 27,484 citations🎓The University of SydneySydney · Australia49 works · 68,350 citations🎓Sun Yat-sen UniversityGuangzhou · China72 works · 9,502 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as Yuchen Wu, Liang Ding, Li Shen and Dacheng Tao. 2026. Reason-KE++: Aligning the Process, Not Just the Outcome, for Faithful LLM Knowledge Editing. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1137

@inproceedings{wu2026reason,
  title = {{Reason-KE++: Aligning the Process, Not Just the Outcome, for Faithful LLM Knowledge Editing}},
  author = {Yuchen Wu, Liang Ding, Li Shen and Dacheng Tao},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1137},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1137}
}

References

28 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Afra Feyza Akyürek, Eric Pan, Garry Kuwanto, and Derry Wijaya. 2023. Dune: Dataset for unified editing. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1847–1861. find
  2. Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. 2024. Evaluating the ripple effects of knowledge editing in language models. Transactions of the Association for Computational Linguistics, 12:283–298. find
  3. DeepSeek-AI. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948.
  4. Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, and Xin Wang. 2023. Pokemqa: Programmable knowledge editing for multi-hop question answering. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8069–8083. find
  5. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021. Unsupervised dense information retrieval with contrastive learning. arXiv preprint arXiv:2112.09118.
  6. Yibin Lei, Liang Ding, Yu Cao, Changtong Zan, Andrew Yates, and Dacheng Tao. 2023. Unsupervised dense retrieval with relevance-aware contrastive pre-training. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10932–10940, Toronto, Canada. Association for Computational Lin… find
  7. Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023. Let’s verify step by step. In The twelfth international conference on learning representations.
  8. Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022. What makes good in-context examples for gpt-3? In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures.
  9. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022. Locating and editing factual associations in GPT. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9,… find
  10. Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. 2023. Mass-editing memory in a transformer. In The Eleventh International Conference on Learning Representations 2023. find
  11. Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning. 2022. Fast model editing at scale. In International Conference on Learning Representations. find
  12. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, Daniel M. Ziegler, Jacob Hilton, Harrison Kelton, Luke Baird, Shibani Allen, Ryan Lowe, Paul Ndousse, Amanda Askell, Shibani DasSarma, and 3 others. 2022. Tr… — Training language models to follow instructions with human feedback
  13. Keqin Peng, Liang Ding, Yancheng Yuan, Xuebo Liu, Min Zhang, Yuanxin Ouyang, and Dacheng Tao. 2024. Revisiting demonstration selection strategies in in-context learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9090– 91… find
  14. Qwen Team. 2024. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115. find
  15. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023. Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems, 36:53728–53741.
  16. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y.K. Li, Y. Wu, and Daya Guo. 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300.
  17. Yucheng Shi, Qiaoyu Tan, Xuansheng Wu, Shaochen Zhong, Kaixiong Zhou, and Ninghao Liu. 2024. Retrieval-enhanced knowledge editing in language models for multi-hop question answering. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pages 2056–2066. find
  18. Changyue Wang, Weihang Su, Qingyao Ai, and Yiqun Liu. 2025. Knowledge editing through chain-of-thought. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. find
  19. Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhifang Sui. 2024a. Math-shepherd: Verify and reinforce llms step-by-step without human annotations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pape…
  20. Xiaohan Wang, Siyuan Cheng, Zhoubo Li, Shumin Deng, and Huajun Chen. 2024b. Easyedit: An easy-to-use knowledge editing framework for large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations). find
  21. Yuchen Wu, Liang Ding, Li Shen, and Dacheng Tao. 2025a. Edit once, update everywhere: A simple framework for cross-lingual knowledge synchronization in LLMs. In Findings of the Association for Computational Linguistics: ACL 2025, Vienna, Austria. Association for Computational Linguistics. find
  22. Yuchen Wu, Liang Ding, Li Shen, and Dacheng Tao. 2025b. Robust knowledge editing via explicit reasoning chains for distractor-resilient multi-hop qa. In Findings of the Association for Computational Linguistics: EMNLP 2025. find
  23. Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023. Editing large language models: Problems, methods, and opportunities. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. find
  24. Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, and Zhumin Chen. 2024. Uncovering overfitting in large language model editing. arXiv preprint arXiv:2410.07819. — [arXiv:2410.07819](https://arxiv.org/abs/2410.07819) find
  25. Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, and 3 others. 2023. A survey of large language models. arXiv…
  26. Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. 2023. Can we edit factual knowledge by in-context learning? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. find
  27. Zexuan Zhong, Zhengxuan Wu, Christopher D Manning, Christopher Potts, and Danqi Chen. 2023. Mquake: Assessing knowledge editing in language models via multi-hop questions. arXiv preprint arXiv:2305.14795. find
  28. Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, and Sanjiv Kumar. 2020. Modifying memories in transformer models. arXiv preprint arXiv:2012.00363. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/wu-2026-reason-ke-aligning-process-not/