Continual Safety Alignment via Gradient-Based Sample Selection
Large language models require continuous adaptation to new tasks while preserving safety alignment. However, fine-tuning on even benign data often compromises safety behaviors, including refusal of harmful requests, truthfulness, and commonsense reasoning. We investigate which training samples cause alignment drift through a data-centric lens. Our empirical analysis shows samples contribute unequally: high-gradient samples cause greater safety degradation and drive models toward pretrained distributions, while moderate-gradient samples enable task learning with minimal alignment loss. We propose gradient-based sample selection that filters high-gradient samples during fine-tuning. Across ...
| Authors | Thong Bach, Dung Nguyen, Thao Minh Le et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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