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From Solver Feedback to Faithful Plans: Multi-Role Reinforcement Learning for Symbolic Planning
Reliable planning requires converting natural-language instructions into executable symbolic specifications, yet large language models remain brittle without costly PDDL annotations and may exploit solver success in semantically unfaithful ways. We study how to learn faithful natural-language-to-PDD
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