From Solver Feedback to Faithful Plans: Multi-Role Reinforcement Learning for Symbolic Planning
The paper introduces a reinforcement learning framework where one large language model plays three coordinated roles: Actor for generating planning specifications, Judge for verifying solver success, and Editor for repairing failures. This zero-annotation approach uses deterministic solver feedback to train the model to produce faithful symbolic plans that are both executable and semantically correct.