Learning How to Search for Plans with Exponentially Less Space
This study addresses the memory bottleneck encountered by heuristic planning search in exponential state spaces by proposing an indexed search strategy that leverages large language models (LLMs) to guide the learning of efficient planning policies. Methodologically, it employs a counterexample-guided iterative LLM training mechanism integrated with depth-first search and automated termination verification. Theoretically, this work provides the first proof that structural termination guarantees polynomial space complexity, enabling the solution of large-scale problems with minimal backtracking points. Experimental evaluations on benchmarks such as IPC 2023 demonstrate that the proposed approach solves over 90% of tasks, outperforming mainstream planners. Notably, the vast majority of tasks are completed within one second and under 100 MiB of memory, highlighting its exceptional computational efficiency.