Efficient Test-time Inference for Generative Planning Models with OCL Search

This paper presents an optimized inference method for generative planning models using a modified Open-Closed List (OCL) search algorithm. The approach integrates a generative model for rapid rollouts and a heuristic model for prioritizing reasoning paths, resulting in improved computational efficiency and solution quality across various combinatorial planning domains. This advancement is significant for practitioners as it enhances the performance of generative models without requiring extensive computational resources during inference.