@inproceedings{tan-etal-2026-q,
title = "{Q}-realign: Piggybacking Realignment on Quantization for Safe and Efficient {LLM} Deployment",
author = "Tan, Qitao and
Song, Xiaoying and
Cheng, Ningxi and
Liu, Ninghao and
Zhai, Xiaoming and
Hong, Lingzi and
Wang, Yanzhi and
Xiang, Zhen and
Yuan, Geng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.63/",
pages = "1276--1291",
ISBN = "979-8-89176-462-0",
abstract = "Public large language models (LLMs) are typically safety-aligned during pretraining, but task-specific fine-tuning often erodes this alignment, introducing significant safety risks. Existing defenses are largely coupled with the fine-tuning process, incurring high computational overhead and complex workflows. To address these challenges, we propose Q-realign, a post-hoc defense based on quantization. Departing from the conventional view of quantization as a safety-eroding compromise, we reframe it as a dual-purpose mechanism for both compression and safety recovery. This allows Q-realign to decouple safety alignment from fine-tuning and naturally piggyback onto modern deployment pipelines. Experiments across multiple models and datasets demonstrate that our method substantially reduces unsafe behaviors while preserving task performance, significantly improving memory and GPU efficiency. Notably, our approach can recover the safety alignment of a fine-tuned 7B LLM on a single RTX 4090 within 40 minutes. Overall, our work provides a practical, turnkey solution for safety-aware deployment. Our code is released at https://anonymous.4open.science/r/Q-realign-copy-0D71."
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<abstract>Public large language models (LLMs) are typically safety-aligned during pretraining, but task-specific fine-tuning often erodes this alignment, introducing significant safety risks. Existing defenses are largely coupled with the fine-tuning process, incurring high computational overhead and complex workflows. To address these challenges, we propose Q-realign, a post-hoc defense based on quantization. Departing from the conventional view of quantization as a safety-eroding compromise, we reframe it as a dual-purpose mechanism for both compression and safety recovery. This allows Q-realign to decouple safety alignment from fine-tuning and naturally piggyback onto modern deployment pipelines. Experiments across multiple models and datasets demonstrate that our method substantially reduces unsafe behaviors while preserving task performance, significantly improving memory and GPU efficiency. Notably, our approach can recover the safety alignment of a fine-tuned 7B LLM on a single RTX 4090 within 40 minutes. Overall, our work provides a practical, turnkey solution for safety-aware deployment. Our code is released at https://anonymous.4open.science/r/Q-realign-copy-0D71.</abstract>
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%0 Conference Proceedings
%T Q-realign: Piggybacking Realignment on Quantization for Safe and Efficient LLM Deployment
%A Tan, Qitao
%A Song, Xiaoying
%A Cheng, Ningxi
%A Liu, Ninghao
%A Zhai, Xiaoming
%A Hong, Lingzi
%A Wang, Yanzhi
%A Xiang, Zhen
%A Yuan, Geng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F tan-etal-2026-q
%X Public large language models (LLMs) are typically safety-aligned during pretraining, but task-specific fine-tuning often erodes this alignment, introducing significant safety risks. Existing defenses are largely coupled with the fine-tuning process, incurring high computational overhead and complex workflows. To address these challenges, we propose Q-realign, a post-hoc defense based on quantization. Departing from the conventional view of quantization as a safety-eroding compromise, we reframe it as a dual-purpose mechanism for both compression and safety recovery. This allows Q-realign to decouple safety alignment from fine-tuning and naturally piggyback onto modern deployment pipelines. Experiments across multiple models and datasets demonstrate that our method substantially reduces unsafe behaviors while preserving task performance, significantly improving memory and GPU efficiency. Notably, our approach can recover the safety alignment of a fine-tuned 7B LLM on a single RTX 4090 within 40 minutes. Overall, our work provides a practical, turnkey solution for safety-aware deployment. Our code is released at https://anonymous.4open.science/r/Q-realign-copy-0D71.
%U https://aclanthology.org/2026.findings-emnlp.63/
%P 1276-1291
Markdown (Informal)
[Q-realign: Piggybacking Realignment on Quantization for Safe and Efficient LLM Deployment](https://aclanthology.org/2026.findings-emnlp.63/) (Tan et al., Findings 2026)
ACL
- Qitao Tan, Xiaoying Song, Ningxi Cheng, Ninghao Liu, Xiaoming Zhai, Lingzi Hong, Yanzhi Wang, Zhen Xiang, and Geng Yuan. 2026. Q-realign: Piggybacking Realignment on Quantization for Safe and Efficient LLM Deployment. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 1276–1291, Budapest, Hungary. Association for Computational Linguistics.