rule-extraction

J-Miner recovers executable decision knowledge from fine-tuned language-model classifiers by mining named concepts and learning decision rules, enabling inspection and transfer to lightweight models with high fidelity.

This paper proposes a method to extract an executable Prolog program from a deep reinforcement learning policy, providing theoretical guarantees on return and fidelity, enabling interpretability and manual editing.