GR-SAP: Generative Replay for Safety Alignment Preservation during Fine-Tuning

Abstract:Recent studies show that the safety alignment of large language models (LLMs) can be easily compromised even by seemingly non-adversarial fine-tuning. To preserve safety alignment during fine-tuning, a widely used strategy is to jointly optimize safety and task objectives by mixing in the original alignment data, which is typically inaccessible even for open-weight LLMs. Inspired by generative replay in continual learning, we propose Generative Replay for Safety Alignment Preservation (GR-SAP), a unified framework that synthesizes domain-specific alignment data from LLMs and integrate them during downstream adaption to preserve safety alignment. Theoretical and empirical analyses demonstrate this synthetic data serves as a reliable proxy for the original alignment data. Experiments across various models and downstream tasks show that GR-SAP substantially mitigates fine-tuning-induced safety degradation while maintaining comparable downstream performance. Our code is available at this https URL.

Computation and Language (cs.CL)

arXiv:2603.10243 [cs.CL]

(or arXiv:2603.10243v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.10243 Focus to learn more arXiv-issued DOI via DataCite

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