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Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations — arXiv:2608.12062

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.12062
License (as archived): CC0-1.0
Archived: 2026-10-19T05:16:24.013Z
SHA-256 of the archived text: bfa0c0b6aff5194c793716b2f364b6cfc47de4d8258c320bcd23e9f2dece93d1
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How to cite

Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations — arXiv:2608.12062 — arXiv (en), https://arxiv.org/abs/2608.12062 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_32036dfad49f (Metraipolis archive dc_32036dfad49f, sha256 bfa0c0b6aff5194c793716b2f364b6cfc47de4d8258c320bcd23e9f2dece93d1)

BibTeX

@misc{dc32036dfad49f,
  title = {Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations — arXiv:2608.12062},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.12062},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_32036dfad49f; sha256 bfa0c0b6aff5194c793716b2f364b6cfc47de4d8258c320bcd23e9f2dece93d1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.12062. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-13 in cs.AI).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.12062 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:08:46.757Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-13?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).