Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations
Developing dialogue systems capable of engaging in multi-turn, goal-oriented conversations remains a significant challenge, especially in specialized domains with limited data. This research proposes a novel framework called Preference Tree Optimization (PTO), designed to iteratively improve agent models in such dialogue systems, by generating preference data using a method called Preference Tree with Look-Ahead. Focusing on Motivational Interviewing (MI) -- a counseling technique aimed at facil
Record details
Published: 12 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy
Retrieved: 13 August 2026
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ethics.ai (12 August 2026), “Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations,” evidence record 19040, https://ethics.ai/record/19040 (originally published by arXiv cs.AI).
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