Continual Safety Alignment via Gradient-Based Sample Selection

ACL ARR 2026 January Submission8026 Authors

Keywords: Safety alignment, continual learning, fine-tuning, sample selection, catastrophic forgetting

Abstract: Large language models require continuous adaptation to new tasks while preserving safety alignment. However, fine-tuning on even benign data often compromises safety behaviors. We investigate which training samples cause alignment drift through a data-centric lens. Our experiments show 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. This connects to the elasticity phenomenon—high-gradient samples activate the reversion force pulling models toward pretrained behavior. We propose gradient-based sample selection that filters high-gradient samples during fine-tuning. Across multiple model families on continual domain tasks, our method substantially improves alignment preservation while maintaining competitive task performance, without requiring curated safe data or architectural modifications.

Paper Type: Long

Research Area: Safety and Alignment in LLMs

Research Area Keywords: fine-tuning; continual learning; safety and alignment

Contribution Types: NLP engineering experiment, Approaches to low-resource settings

Languages Studied: English

Submission Number: 8026