GR-SAP: Generative Replay for Safety Alignment Preservation during Fine-Tuning
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 demonstr...
| Authors | Zhouxiang Fang, Jiawei Zhou, Hanjie Chen |
|---|---|
| Primary category | cs.CL |
| Published | 10 March 2026 |
| Last updated at source | 24 August 2026 |
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