用改进的OCL搜索提升生成式规划模型的推理效率与质量
Efficient Test-time Inference for Generative Planning Models with OCL Search
- 在经典OCL搜索中融入生成模型和启发式模型，实现高效推理
- 在多个组合规划任务中，计算效率与解质量均优于神经符号与传统求解器
- 适合追求高效推理的生成式规划研究者或工业应用开发者
生成模型已成为人工智能规划的强大范式，但其性能仍受限于训练数据分布。一种提升生成解的方法是通过扩展测试时计算来优化推理过程，而更高效的方式是优化推理本身。本文提出一种改进的开放-封闭列表（OCL）搜索算法，该方法融合了两个学习组件：快速从中间状态进行模拟的生成模型，以及对候选推理路径进行优先排序的启发式模型。关键贡献包括新的探索控制机制，以及将学习模型有效集成到OCL框架中的方法。在多个组合规划领域中，该方法在计算效率和解质量上均超越了现有的神经符号搜索基线和经典求解器。
原文摘要 · Abstract (English)
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.
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