SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection

• URL: http://arxiv.org/abs/2601.02871v2
• Date: Thu, 08 Jan 2026 04:14:02 GMT
• Title: SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection
• Authors: Zhiyong Cao, Dunqiang Liu, Qi Dai, Haojun Xu, Huaiyan Xu, Huan He, Yafei Liu, Siyuan Liu, XiaoLin Lin, Ke Ma, Ruqian Shi, Sijia Yao, Hao Wang, Sicheng Zhou,
• Abstract summary: SimRPD is a three-stage framework for training recruitment proactive dialogue agents.<n>First, we develop a high-fidelity user simulator to synthesize large-scale conversational data.<n>Then, we introduce a multi-dimensional evaluation framework based on Chain-of-Intention.<n>Finally, we train the recruitment proactive dialogue agent on the selected dataset.
• Score: 19.80033581334685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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 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.

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