  We study the problem of Continual Distillation Learning (CDL) that considers
Knowledge Distillation (KD) in the Continual Learning (CL) setup. A teacher
model and a student model need to learn a sequence of tasks, and the knowledge
of the teacher model will be distilled to the student to improve the student
model. We introduce a novel method named CDL-Prompt that utilizes prompt-based
continual learning models to build the teacher-student model. We investigate
how to utilize the prompts of the teacher model in the student model for
knowledge distillation, and propose an attention-based prompt mapping scheme to
use the teacher prompts for the student. We demonstrate that our method can be
applied to different prompt-based continual learning models such as L2P,
DualPrompt and CODA-Prompt to improve their performance using powerful teacher
models. Although recent CL methods focus on prompt learning, we show that our
method can be utilized to build efficient CL models using prompt-based
knowledge distillation.
