What Should World Models Forget? Stratified Retention for Continual Adaptation

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What Should World Models Forget? Stratified Retention for Continual Adaptation

Continual learning treats degradation on previously seen data as evidence of failure, a convention inherited from settings with a stationary prediction target, where a correct label remains correct indefinitely. World models do not satisfy this condition. Their prediction target is the environment, which changes, so knowledge that was accurate when acquired may later become false, and discarding it is required behavior rather than a defect. Non-stationary ground truth is well studied in the conc

Source: ArXiv AI