Boundary-Calibrated Intervention for Efficient LLM Post-Training

This research addresses the inefficiency of repeated LLM post-training by treating past updates as conditional experience transfer. It introduces a method to determine which prior training evidence remains actionable after a parent model has evolved, preventing compute waste and trajectory degradation.