#open-ended-learning News & Analysis

An Information-Theoretic Definition for Open-Ended Learning

Researchers propose a novel information-theoretic framework for defining open-ended learning in AI systems, introducing the concept of "bit-equivalent" to measure information required for reward attainment. The work establishes formal criteria for open-endedness—linear growth in bit-equivalent—and demonstrates that classical bandit environments fail this threshold while presenting both a qualifying environment and an algorithm achieving open-ended learning.