rule-learning

RimRule proposes a neuro-symbolic method that distills compact, interpretable rules from failure traces using the Minimum Description Length principle, improving LLM tool-use performance without modifying weights, and demonstrating rule portability across models.

JERP introduces a method for LLM agents to jointly learn interpretable natural-language rules and update policy parameters from the same interaction trajectories, improving performance on AlfWorld and WebShop while maintaining inspectability.

This paper introduces gammaILP, a fully differentiable framework for learning first-order rules directly from image data without label leakage, addressing challenges in symbol grounding and predicate invention.