Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations

2026-08-12

AI Summary

This work addresses the challenge of building multi-turn, goal-oriented dialogue systems in data-scarce settings such as motivational interviewing by proposing a Preference Tree Optimization (PTO) framework. PTO integrates virtual patient simulation, forward-looking tree search, and an Oracle evaluator to generate high-quality preference data, which is then leveraged through iterative Direct Preference Optimization (DPO) to enhance agent decision-making. Introducing, for the first time, a preference tree structure coupled with a forward simulation mechanism, PTO effectively mitigates data scarcity while enabling long-horizon planning and fine-grained policy optimization. Experimental results demonstrate that models trained with PTO significantly outperform baselines on key metrics including conversational satisfaction and therapeutic alliance, with deeper lookahead configurations yielding more stable and higher-performing dialogue behavior.

Abstract

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 facilitating behavioral change -- we leverage virtual patients and an oracle evaluator to simulate conversations and generate rich preference datasets. By combining this method with Direct Preference Optimization (DPO), we aim to enhance the agent's decision-making capabilities over iterative training cycles. The proposed framework addresses data scarcity and advances the development of more nuanced and effective dialogue systems in goal-oriented domains. Experimental evaluations demonstrate that the PTO framework enhances dialogue agents' performance in goal-oriented conversations within the domain of Motivational Interviewing (MI). Models trained with PTO consistently outperformed the baseline in key metrics such as session satisfaction and working alliance. Additionally, incorporating look-ahead simulations led to improved long-term planning and more effective conversational strategies, with deeper look-ahead configurations yielding the most stable and high-scoring results.

Problem

• goal-oriented dialogue
• data scarcity
• Motivational Interviewing
• preference optimization
• dialogue systems

Innovation

• Preference Tree Optimization
• Look-Ahead Simulation
• Direct Preference Optimization
• Goal-Oriented Dialogue
• Motivational Interviewing