  The mainstream paradigm behind continual learning has been to adapt the model
parameters to non-stationary data distributions, where catastrophic forgetting
is the central challenge. Typical methods rely on a rehearsal buffer or known
task identity at test time to retrieve learned knowledge and address
forgetting, while this work presents a new paradigm for continual learning that
aims to train a more succinct memory system without accessing task identity at
test time. Our method learns to dynamically prompt (L2P) a pre-trained model to
learn tasks sequentially under different task transitions. In our proposed
framework, prompts are small learnable parameters, which are maintained in a
memory space. The objective is to optimize prompts to instruct the model
prediction and explicitly manage task-invariant and task-specific knowledge
while maintaining model plasticity. We conduct comprehensive experiments under
popular image classification benchmarks with different challenging continual
learning settings, where L2P consistently outperforms prior state-of-the-art
methods. Surprisingly, L2P achieves competitive results against rehearsal-based
methods even without a rehearsal buffer and is directly applicable to
challenging task-agnostic continual learning. Source code is available at
https://github.com/google-research/l2p.
