World models need stratified forgetting strategies that preserve physical invariants while quickly adapting to environmental changes—standard continual learning metrics fail to capture this distinction and incorrectly reward frozen models.
This paper addresses a fundamental problem in continual learning for world models: knowing what to forget. Unlike traditional learning where correct labels stay correct, world models operate in changing environments where outdated knowledge must be discarded.