Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences

Understand

When do machine learning systems fail to generalize, and what mechanisms could improve their generalization? Here, we draw inspiration from cognitive science to argue that one weakness of parametric machine learning systems is their failure to exhibit latent learning -- learning information that is not relevant to the task at hand, but that might be useful in a future task.

• We show how this perspective links failures ranging from the reversal curse in language modeling to new findings on agent-based navigation.
• We then highlight how cognitive science points to episodic memory as a potential part of the solution to these issues.
• Correspondingly, we show that a system with an oracle retrieval mechanism can use learning experiences more flexibly to generalize better across many of these challenges.