@inproceedings{cao-etal-2026-simrpd,
    title = "{S}im{RPD}: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection",
    author = "Cao, Zhiyong  and
      Liu, Dunqiang  and
      Dai, Qi  and
      Xu, Haojun  and
      Khor, Huai Yuen  and
      Wang, Hao  and
      He, Huan  and
      Liu, Yafei  and
      Ma, Ke  and
      Shi, Ruqian  and
      Zhou, Sicheng  and
      Yao, Sijia",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.95/",
    pages = "1359--1377",
    ISBN = "979-8-89176-394-4",
    abstract = "Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion. Although supervised fine-tuning and reinforcement learning have proven effective for training such agents, their performance is heavily constrained by the scarcity of high-quality, goal-oriented domain-specific training data. To address this challenge, we propose $\textbf{SimRPD}$, a three-stage framework for training recruitment proactive dialogue agents. First, we develop a high-fidelity user simulator to synthesize large-scale conversational data through multi-turn online dialogue. Then we introduce a multi-dimensional evaluation framework based on $\textbf{Chain-of-Intention (CoI)}$ to comprehensively assess the simulator and effectively select high-quality data, incorporating both global-level and instance-level metrics. Finally, we train the recruitment proactive dialogue agent on the selected dataset. Experiments in a real-world recruitment scenario demonstrate that SimRPD outperforms existing simulator-based data selection strategies, highlighting its practical value for industrial deployment and its potential applicability to other business-oriented dialogue scenarios."
}