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

Generative models have emerged as a powerful paradigm for AI planning, yet their performance remains constrained by 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 inferential process itself. In this paper, we show that a modified version of a classical Open-Closed List (OCL) search provides just such an efficient inferential procedure. Our algorithm synergizes two learned components: a generative model that performs fast rollouts from specific reasoning paths and a value model that manages which of many possible reasoning lines to follow. We present novel contributions in exploration control and how learned models are integrated within the OCL framework. Experimental evaluation across multiple combinatorial planning domains shows that our approach consistently outperforms baseline search algorithms in both computational efficiency and solution quality.