  In the field of large language models (LLMs), Knowledge Distillation (KD) is
a critical technique for transferring capabilities from teacher models to
student models. However, existing KD methods face limitations and challenges in
distillation of LLMs, including efficiency and insufficient measurement
capabilities of traditional KL divergence. It is shown that LLMs can serve as
an implicit reward function, which we define as a supplement to KL divergence.
In this work, we propose Direct Preference Knowledge Distillation (DPKD) for
LLMs. DPKD utilizes distribution divergence to represent the preference loss
and implicit reward function. We re-formulate KD of LLMs into two stages: first
optimizing and objective consisting of implicit reward and reverse KL
divergence and then improving the preference probability of teacher outputs
over student outputs. We conducted experiments and analysis on various datasets
with LLM parameters ranging from 120M to 13B and demonstrate the broad
applicability and effectiveness of our DPKD approach. Meanwhile, we prove the
value and effectiveness of the introduced implicit reward and output preference
in KD through experiments and theoretical analysis. The DPKD method outperforms
the baseline method in both output response precision and exact match
percentage. Code and data are available at https://aka.ms/dpkd.
