  Recently, multi-modal content generation has attracted lots of attention from
researchers by investigating the utilization of visual instruction tuning based
on large language models (LLMs). To enhance the performance and generalization
ability of such LLMs, the practice of distilling knowledge from pretrained
multi-modal models (a.k.a. teachers) to more compact multi-modal LLMs
(students) has gained considerable interest. However, the prevailing paradigm
of instructiontuning in multi-modal LLMs knowledge distillation is
resource-intensive and unidirectional, neglecting the potential for mutual
feedback between the student and teacher models. Thus, we propose an innovative
Competitive Multi-modal Distillation framework (CoMD), which captures
bidirectional feedback between teacher and student models and continually
updates the multi-modal capabilities that the student model has learned. It
comprises two stages: multi-modal pre-training and multi-modal competitive
distillation. The first stage pre-trains the student model on a large number of
filtered multi-modal datasets. The second stage facilitates a bidirectional
knowledge transfer between the student and teacher models. Our experimental
analysis of diverse datasets shows that our knowledge transfer method
consistently improves the capabilities of the student model. Finally, the
7B-sized student model after four distillations surpassed the current
state-of-the-art model LLaVA-13B on the ScienceQA and LLaVA Test dataset, also
outperforms other strong baselines in the zero-shot setting.
