How to make AI planners faster without retraining?

Generative models for AI planning often struggle beyond their training data. Instead of scaling up compute at test time, this work revives the Open-Closed List search algorithm and pairs it with two learned components: a fast rollout model and a heuristic guide. The hybrid approach outperforms neurosymbolic baselines and classical solvers on combinatorial planning tasks—achieving better solutions with less computation.