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A Reinforcement Learning-Based Facilitator for Simulated Group Motivational Interviewing

Researchers from Sorbonne Université and CNRS have developed the first reinforcement learning-based dialogue manager for simulated group motivational interviewing, using a discrete Soft-Actor-Critic policy to select therapist acts and LLM-generated utterances. The system, presented at SIGDIAL 2026, outperformed four LLM-based baselines by generating more directive acts and adapting to participant profiles, particularly in groups with an open-to-change participant.

read1 min views1 publishedJul 21, 2026
A Reinforcement Learning-Based Facilitator for Simulated Group Motivational Interviewing
Image: Aclanthology (auto-discovered)
[A Reinforcement Learning-Based Facilitator for Simulated Group Motivational Interviewing](https://aclanthology.org/2026.sigdial-1.61.pdf)

[Alafate Abulimiti](/people/alafate-abulimiti/unverified/),
[Vladislav Maraev](/people/vladislav-maraev/unverified/),
[Agnès Helme-Guizon](/people/agnes-helme-guizon/unverified/),
[Catherine Pelachaud](/people/catherine-pelachaud/)
Abstract

Motivational Interviewing (MI) is a widely validated approach to behavior change, but existing virtual MI agents operate only in one-on-one settings, ignoring the cost-effectiveness and peer-support dynamics of group MI. We present a simulation environment and reinforcement learning (RL) based dialogue manager for group MI, in which a discrete Soft-Actor-Critic (SAC) policy selects therapist dialogue acts and a large language model generates utterances, with two LLM-prompted patient agents as interlocutors. Our model supports adaptation to different participant profiles. We compared our dialogue manager with four LLM-based ones at the dialogue acts level. We observed that RL yields a significantly different therapist policy, which showed the tendency to generate more directive acts and adapt to varying group compositions. Participant profile adaptation was the strongest in groups containing an open-to-change participant.- Anthology ID:

- 2026.sigdial-1.61
- Volume:
[Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue](/volumes/2026.sigdial-1/)- Month:
  • August
  • Year:
  • 2026
  • Address:
  • Atlanta, Georgia, USA
- Editors:
[Jinho D. Choi](/people/jinho-d-choi/),[Yun-Nung Chen](/people/yun-nung-chen/),[Kotaro Funakoshi](/people/kotaro-funakoshi/),[Ali Emami](/people/ali-emami/)- Venue:
[SIGDIAL](/venues/sigdial/)- SIG:
[SIGDIAL](/sigs/sigdial/)- Publisher:
  • Association for Computational Linguistics
- Note:
- Pages:
  • 871–890
- Language:
- URL:
[https://aclanthology.org/2026.sigdial-1.61/](https://aclanthology.org/2026.sigdial-1.61/)- DOI:
- Cite (ACL):
[A Reinforcement Learning-Based Facilitator for Simulated Group Motivational Interviewing](https://aclanthology.org/2026.sigdial-1.61/)(Abulimiti et al., SIGDIAL 2026)- PDF:
[https://aclanthology.org/2026.sigdial-1.61.pdf](https://aclanthology.org/2026.sigdial-1.61.pdf)
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