A model's internal state and external training conditions interact in complex, non-additive ways—the same intervention can help or hurt depending on what happens next, which matters for understanding continual learning and model adaptation.
This paper studies how a machine learning model's current state interacts with future training dynamics. The authors develop methods to measure and manipulate learning states—like classifier weights and historical information—to understand when interventions help or hurt performance.