Review for NeurIPS paper: Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning

Weaknesses: My main concern with the submission is that the evaluation scenario OSAKA seems too specific and designed primarily for a set of algorithms in between Meta- & Continual-Learning while failing to make a broader argument for other approaches to Continual Learning. While certain aspects of OSAKA are certainly desirable (OOD tasks, Unknown task changes, Online Evaluation) there is a strong assumption made in allowing for Pre-training that may not be adequate in certain CL settings, limiting the generality of OSAKA. Furthermore, it is unclear how aspects such as controllable non-stationarity would be implemented in Reinforcement Learning. Furthermore, I personally feel that if task-revisiting is to be implemented, new OOD tasks should be designed in a way that explicitly re-uses skills that can be learned on a previous problem in a novel setting, instead of merely re-visiting the problem without modification. The problem with this assumption in general is that Catastrophic Forgetting is significantly reduced through an implicit form of replay provided by the environment, making it difficult to tell to which extent catastrophic forgetting is actually a problem of these algorithms.