Preserving Safety During Sequential Fine-Tuning: A Factorial and Geometric Analysis of Replay

arXiv:2610.38001v1 Announce Type: new Abstract: Benign fine-tuning can erode safety alignment in large language models, and sequential adaptation substantially compounds this risk. Prior work mitigates this erosion by replaying harmful-prompt refusals and benign-compliance examples, while other work instead matches base-model logits via distillation. However, the individual contributions of supervised exposure and distillation to safety behavior and refusal geometry during sequential adaptation remain unclear. We factor these two strategies apart and study their behavioral and geometric effects during sequential LoRA fine-tuning of Mistral-7B and Llama-3-8B across diverse task sequences (code generation, German-language summarization, and science question-answering).