On-Policy Distillation of Language Models: Learning from Self-Generated
  Mistakes
Authors: name: Rishabh Agarwal, name: Nino Vieillard, name: Yongchao Zhou, name: Piotr Stanczyk, name: Sabela Ramos, name: Matthieu Geist, name: Olivier Bachem

Knowledge distillation (KD) is widely used for compressing a teacher model to
reduce its inference cost and memory footprint, by training a smaller student
model. However, current KD methods for auto-regressive sequence models suffer
from distribution mismatch between output sequences seen during training and
those generated by the student during inference. To address this issue, we
introduce Generalized Knowledge Distillation (GKD). Instead of solely relying
on a fixed set of output sequences, GKD trains the student on its
self-generated output sequences by leveraging feedback from the teacher on such
sequences. Unlike supervised KD approaches, GKD also offers the flexibility to
employ alternative loss functions between the student and teacher, which can be
useful when the student lacks the expressivity to mimic the teacher's
distribution. Furthermore, GKD facilitates the seamless integration of
distillation with RL fine-tuning (RLHF). We demonstrate the efficacy of GKD for
distilling auto-regressive language models on summarization, translation, and
arithmetic reasoning tasks, and task-agnostic distillation for
instruction-tuning.