arXiv:2610.34677 · v1
Curriculum-Guided Acquisition of Hierarchical Task Knowledge from LLM-Generated Experience
摘要
Hierarchical task knowledge enables efficient problem solving by allowing previously acquired skills to be reused across tasks and problem instances. In this paper, we investigate curriculum-guided acquisition of hierarchical task knowledge in an HTN learning framework. We compare three approaches: online learning during problem solving, human-designed curricula, and an automated curriculum generation procedure that derives learning experiences from verified LLM-generated solution traces. The proposed procedure solves small training problems with an LLM, extracts task accomplishment information, and transforms it into ordered sequences of state-task learning problems for HTN method acquisition. We evaluate the three approaches across multiple planning domains. The results demonstrate that automatically generated curricula recover much of the benefit of expert-designed curricula while substantially reducing the token cost of online hierarchical knowledge acquisition.
作者备注：15 pages, 2 figures, 1 table. Presented at the Thirteenth Annual Conference on Advances in Cognitive Systems (ACS 2026)
期刊信息：Xu, Y., and Munoz-Avila, H. (2026). Curriculum-Guided Acquisition of Hierarchical Task Knowledge from LLM-Generated Experience. Proceedings of the Thirteenth Annual Conference on Advances in Cognitive Systems (ACS 2026)