Safety Offset Learning: When Benign Fine-Tuning Misaligns LLMs.

arXiv:2610.25611v1 Announce Type: cross Abstract: Safety risks from fine-tuning are commonly characterized by the harmfulness of the training data:harmful fine-tuning is considered dangerous, while benign fine-tuning is often assumed to preservealignment. We a