REDaCT: Collapse-Free Unlearning in Large Reasoning Models
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Computer Science > Machine Learning
arXiv:2610.12769 (cs)
[Submitted on 8 Oct 2026]
Title:REDaCT: Collapse-Free Unlearning in Large Reasoning Models
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Abstract:Unlearning in large reasoning models (LRMs) requires removing forget-set information from both the reasoning trace and the final answer. Existing approaches achieve this objective at the cost of reasoning collapse, where the model fails to produce a coherent chain-of-thought (CoT) and does not properly terminate its reasoning process. To address this challenge, we propose REDaCT (REgenerate via Detection and Corrective Tuning), an inference-time unlearning framework for LRMs. REDaCT consists of two key components: (1) a binary leak detector that identifies sentences in the reasoning trace that leak forget-set information, and (2) an embedding optimization module that learns a corrective embedding offline, such that when injected at the first detected leak during inference, it guides subsequent reasoning towards sentences that do not leak forget-set information. Experimental results show that REDaCT completely avoids reasoning collapse, substantially improving reasoning preservation compared to state-of-the-art unlearning methods while maintaining perfect answer forgetting and near-perfect CoT forgetting. Our code is available at: this https URL.
| Comments: | Accepted to Findings of EMNLP 2026. 19 pages, 7 figures, 6 tables. Copyright 2026 Association for Computational Linguistics (ACL). Licensed under CC BY 4.0 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.12769 [cs.LG] |
| (or arXiv:2610.12769v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12769
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled REDaCT: Collapse-Free Unlearning in Large Reasoning Models, by Joshua Maccoby and 1 other authors
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