An Information-Theoretic Definition for Open-Ended Learning

2026-06-06 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new information-theoretic definition for open-ended learning is proposed, addressing the lack of a coherent framework for AI systems that continually expand capabilities in dynamic environments. This definition introduces the "bit-equivalent," a novel concept quantifying the information required to achieve each level of expected reward. An environment is classified as open-ended if an agent can attain linear growth in its bit-equivalent. The authors demonstrate that classical bandit environments do not meet this criterion for open-endedness. They then formulate a specific bandit environment that is open-ended according to their definition and present an algorithm capable of achieving open-ended learning within this newly defined environment. This work provides a foundational theory for exploring open-ended environments.

Key takeaway

For AI scientists developing systems intended for continuous capability expansion, this information-theoretic definition offers a rigorous framework. You should evaluate your environment designs against the "bit-equivalent" linear growth criterion to determine true open-endedness. This provides a concrete metric beyond qualitative assessments, guiding the development of algorithms capable of sustained, adaptive learning in complex, dynamic settings. Consider applying this framework to benchmark and compare different approaches to open-ended AI.

Key insights

Open-ended learning is defined by linear growth in "bit-equivalent," quantifying information for expected reward attainment.

Principles

• Open-ended environments allow linear bit-equivalent growth.
• Classical bandit environments are not open-ended.
• Information-theoretic metrics can define learning progress.

Topics

• Open-Ended Learning
• Information Theory
• Bit-Equivalent
• Bandit Environments
• Machine Learning
• Artificial Intelligence

Best for: Research Scientist, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.