@inproceedings{an-etal-2026-stagepilot,
title = "{S}tage{P}ilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergrooming",
author = "An, Heajun and
Zhang, Qi and
Liu, Minqian and
Zhang, Xinyi and
Lee, Sang Won and
Huang, Lifu and
Wisniewski, Pamela and
Cho, Jin-Hee",
editor = "Choi, Jinho D. and
Chen, Yun-Nung and
Funakoshi, Kotaro and
Emami, Ali",
booktitle = "Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = aug,
year = "2026",
address = "Atlanta, Georgia, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.sigdial-1.37/",
pages = "536--552",
abstract = "Cybergrooming is an evolving threat to youth, requiring proactive educational interventions. We address this by modeling dialogue progression as a structured planning problem over stage-wise interactions. We propose StagePilot, a dialogue framework that separates stage-level planning from response generation, in which the model selects the next stage under constrained transitions and generates responses conditioned on it, enabling coherent and realistic progression. Reinforcement learning is used to learn stage-level policies from offline data, optimizing for both emotional alignment and goal-consistent progression. Our empirical experiments show that StagePilot generates more structured, coherent dialogue trajectories and reduces conversational stagnation compared to baselines; notably, the IQL+AWAC variant reaches the final stage more often while maintaining over 70{\%} positive or neutral responses, yielding a 43{\%} relative improvement."
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<abstract>Cybergrooming is an evolving threat to youth, requiring proactive educational interventions. We address this by modeling dialogue progression as a structured planning problem over stage-wise interactions. We propose StagePilot, a dialogue framework that separates stage-level planning from response generation, in which the model selects the next stage under constrained transitions and generates responses conditioned on it, enabling coherent and realistic progression. Reinforcement learning is used to learn stage-level policies from offline data, optimizing for both emotional alignment and goal-consistent progression. Our empirical experiments show that StagePilot generates more structured, coherent dialogue trajectories and reduces conversational stagnation compared to baselines; notably, the IQL+AWAC variant reaches the final stage more often while maintaining over 70% positive or neutral responses, yielding a 43% relative improvement.</abstract>
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%0 Conference Proceedings
%T StagePilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergrooming
%A An, Heajun
%A Zhang, Qi
%A Liu, Minqian
%A Zhang, Xinyi
%A Lee, Sang Won
%A Huang, Lifu
%A Wisniewski, Pamela
%A Cho, Jin-Hee
%Y Choi, Jinho D.
%Y Chen, Yun-Nung
%Y Funakoshi, Kotaro
%Y Emami, Ali
%S Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2026
%8 August
%I Association for Computational Linguistics
%C Atlanta, Georgia, USA
%F an-etal-2026-stagepilot
%X Cybergrooming is an evolving threat to youth, requiring proactive educational interventions. We address this by modeling dialogue progression as a structured planning problem over stage-wise interactions. We propose StagePilot, a dialogue framework that separates stage-level planning from response generation, in which the model selects the next stage under constrained transitions and generates responses conditioned on it, enabling coherent and realistic progression. Reinforcement learning is used to learn stage-level policies from offline data, optimizing for both emotional alignment and goal-consistent progression. Our empirical experiments show that StagePilot generates more structured, coherent dialogue trajectories and reduces conversational stagnation compared to baselines; notably, the IQL+AWAC variant reaches the final stage more often while maintaining over 70% positive or neutral responses, yielding a 43% relative improvement.
%U https://aclanthology.org/2026.sigdial-1.37/
%P 536-552
Markdown (Informal)
[StagePilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergrooming](https://aclanthology.org/2026.sigdial-1.37/) (An et al., SIGDIAL 2026)
ACL
- Heajun An, Qi Zhang, Minqian Liu, Xinyi Zhang, Sang Won Lee, Lifu Huang, Pamela Wisniewski, and Jin-Hee Cho. 2026. StagePilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergrooming. In Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 536–552, Atlanta, Georgia, USA. Association for Computational Linguistics.