Efficient Test-time Inference for Generative Planning Models
This work addresses the efficiency bottleneck of generative planning models during inference, offering a practical improvement for AI planning tasks.
The paper introduces an efficient test-time inference method for generative planning models using a modified Open-Closed List search, which combines a generative model for fast rollouts and a heuristic model for path prioritization. The approach outperforms neurosymbolic and classical solvers in computational efficiency and solution quality across multiple combinatorial planning domains.
Generative models have emerged as a powerful paradigm for AI planning, yet their performance remains constrained by the training data distribution. One approach is to improve generated solutions during inference by scaling test-time compute. A more efficient alternative is to optimize the inference process itself. In this paper, we show that a modified version of a classical Open-Closed List (OCL) search provides just such an efficient inference procedure. Our algorithm synergizes two learned components: a generative model that performs fast rollouts from intermediate states and a heuristic model that prioritizes among candidate reasoning paths. Key contributions include novel exploration control mechanisms and integration of learned models within the OCL framework. Across multiple combinatorial planning domains, our approach outperforms both neurosymbolic search baselines and classical solvers in computational efficiency and solution quality.