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Boundless Socratic Learning with Language Games

Summary

This position paper explores the concept of Socratic learning, a type of recursive self-improvement in a closed system where an agent learns solely through language interactions. The authors posit three necessary conditions for this: sufficiently informative feedback, broad data coverage, and sufficient capacity. They propose language games as a framework to achieve this, arguing that multiple, narrowly defined games offer better alignment and coverage than a single, universal game. The paper analyzes potential limitations, including feedback misalignment and data drift, while ultimately expressing optimism about the feasibility of open-ended Socratic learning.

原文链接：arxiv.org

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