Actor-judge-editor RL on solver feedback for NL-to-PDDL
Have the model propose a PDDL formalization, judge it with a solver-calibrated score, and edit it under bounded diagnostic feedback, training all three roles with reinforcement learning from solver outcomes rather than annotations.
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Have the model propose a PDDL formalization, judge it with a solver-calibrated score, and edit it under bounded diagnostic feedback, training all three roles with reinforcement learning from solver outcomes rather than annotations. Filed by the drafting stage from From Solver Feedback to Faithful Plans: Multi-Role Reinforcement Learning for Symbolic Planning (2608.21897) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: PlanBench success, faithful-success and semantic-drift rates against LLM+P and other baselines; faithfulness checks separate solver success from task fidelity.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
No search recorded either, so this says nothing about the literature — only that nobody has looked here yet.
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Sources
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