Tag
This paper reveals a counterintuitive phenomenon where correct demonstrations in in-context learning can degrade model accuracy, introducing task preserving perturbations to study the gap between exemplar correctness and utility.
Introduces Self-Distillation Fine-Tuning (SDFT), a method that enables on-policy learning from demonstrations to achieve continual learning without catastrophic forgetting, outperforming supervised fine-tuning.