Humans learn generalizable representations through efficient coding.

Reinforcement learning theory explains human behavior as driven by the goal of maximizing reward. Conventional approaches, however, offer limited insights into how people generalize from past experiences to new situations. Here, we propose refining the classical reinforcement learning framework by incorporating an efficient coding principle, which emphasizes maximizing reward using the simplest necessary representations. This refined framework predicts that intelligent agents, constrained by simpler representations, will inevitably: 1) distill environmental stimuli into fewer, abstract internal states, and 2) detect and utilize rewarding environmental features. Consequently, complex stimuli are mapped to compact representations, forming the foundation for generalization. We tested this idea in two experiments that examined human generalization. Our findings reveal that while conventional models fall short in generalization, models incorporating efficient coding achieve human-level performance. We argue that the classical RL objective, augmented with efficient coding, represents a more comprehensive computational framework for understanding human behavior in both learning and generalization.