SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915715225747456 |
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| author | Cao, Zhiyong Liu, Dunqiang Dai, Qi Xu, Haojun Xu, Huaiyan He, Huan Liu, Yafei Liu, Siyuan Lin, XiaoLin Ma, Ke Shi, Ruqian Yao, Sijia Wang, Hao Zhou, Sicheng |
| author_facet | Cao, Zhiyong Liu, Dunqiang Dai, Qi Xu, Haojun Xu, Huaiyan He, Huan Liu, Yafei Liu, Siyuan Lin, XiaoLin Ma, Ke Shi, Ruqian Yao, Sijia Wang, Hao Zhou, Sicheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02871 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection Cao, Zhiyong Liu, Dunqiang Dai, Qi Xu, Haojun Xu, Huaiyan He, Huan Liu, Yafei Liu, Siyuan Lin, XiaoLin Ma, Ke Shi, Ruqian Yao, Sijia Wang, Hao Zhou, Sicheng Artificial Intelligence 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. |
| title | SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.02871 |