KAIST’s Temporary ‘Jailbreak’ Makes Personalized AI Fine-Tuning Safer

KAIST researchers introduced Buffer-and-Reinforce, a two-stage fine-tuning method that temporarily isolates a language model while it learns private or company data, then strengthens its safeguards. Tests reduced harmful-response rates without materially sacrificing customization, but the evidence remains limited to controlled model evaluations.