  Instruction tuning is now a widely adopted approach to aligning large
multimodal models (LMMs) to follow human intent. It unifies the data format of
vision-language tasks, enabling multi-task joint training. However,
vision-language tasks are constantly being created in practice. Instead of
always re-training LMMs when new tasks arrive, continual learning offers
flexibility for models to continually and efficiently exploit the evolving
data. This work aims to explore the following two questions: 1) Do LMMs still
suffer from catastrophic forgetting in continual instruction tuning? 2) Are the
existing three classes of continual learning methods still applicable to the
continual instruction tuning of LMMs? An extensive study is conducted to
address the above questions. First, we establish the first benchmark in this
setting and reveal that catastrophic forgetting is still observed when
continually instruction-tuning LMMs. However, the multi-task joint instruction
tuning can facilitate the model's continual learning ability and mitigate
forgetting. Second, we integrate and adapt classic continual learning methods
to our context, demonstrating the efficacy of data replay and model expansion
strategies across diverse scenarios. In contrast, regularization-based methods
only perform well on models that have been jointly instruction-tuned on
multiple tasks. Third, we delve into the correlation and forgetting dynamics
between vision-language task pairs and propose task-similarity-informed
regularization and model expansion methods for continual instruction tuning of
LMMs. Experimental results show that our approach consistently boosts the
model's performance.
