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arXiv · 2604.11427

METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues

Haofu Yang·Jiaji Liu·Chen Huang·Faguo Wu·Wenqiang Lei·See-Kiong Ng
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Abstract

Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose \ours, a method that leverages large language models to autonomously induce both strategy actions and planning logic directly from raw transcripts. METRO formalizes expert knowledge into a Strategy Forest, a hierarchical structure that captures both short-term responses (nodes) and long-term strategic foresight (branches). Experimental results across two benchmarks show that METRO demonstrates promising performance, outperforming existing methods by an average of 9%-10%. Our further analysis not only reveals the success behind METRO (strategic behavioral diversity and foresight), but also demonstrates its robust cross-task transferability. This offers new insights into building non-collaborative agents in a cost-effective and scalable way. Our code is available at https://github.com/Humphrey-0125/METRO.

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Haofu Yang, Jiaji Liu, Chen Huang, Faguo Wu, Wenqiang Lei, See-Kiong Ng. 2026-04-13. METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues. https://arxiv.org/abs/2604.11427

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