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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Lior Baruch, Moshe Butman, Kfir Bar, Doron Friedman
• Venue: arXiv: cs.CL
• Published: 2026-08-12
• Collected: 2026-08-13
• URL: http://arxiv.org/abs/2608.12062v1
• PDF: https://arxiv.org/pdf/2608.12062v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The research presents a new framework called Preference Tree Optimization (PTO) for enhancing dialogue systems in specialized domains with limited data. It focuses on Motivational Interviewing (MI), a counseling technique, and uses virtual patients and an oracle evaluator to simulate conversations and generate preference datasets. The PTO combines the Preference Tree with Look-Ahead method and Direct Preference Optimization (DPO) for iterative improvement of agent models. The results show that models trained with PTO outperform baselines in session satisfaction, working alliance, long-term planning, and conversational strategies.

What the Document Says

• The research presents a new framework called Preference Tree Optimization (PTO).
• It focuses on Motivational Interviewing (MI), a counseling technique aimed at facilitating behavioral change.
• The PTO framework uses virtual patients and an oracle evaluator to simulate conversations and generate preference datasets.
• It combines the Preference Tree with Look-Ahead method and Direct Preference Optimization (DPO) for iterative improvement of agent models.
• 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.

Key Concepts Mentioned

• Preference Tree Optimization (PTO)
• Motivational Interviewing (MI)
• dialogue systems
• agent models
• virtual patients
• oracle evaluator

Claims Made by the Document

• 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.

Evidence Used

• Experimental evaluations

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

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Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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.