Simulator-assisted reinforcement learning is a practical way to train multi-turn dialogue agents, but policy optimization can be misled when simulated users provide inconsistent feedback across long interaction trajectories. In this paper, we focus on a simulator-side question: can structured latent state tracking improve measured simulator consistency and provide useful process supervision for dialogue policy training? Motivated by a standard model-based RL simulation-bound perspective, we introduce **DPS**, a framework that decouples task-conditioned privileged state tracking from surface utterance realization. These privileged states are hidden from the agent but used during training to derive dense, state-aware process rewards that complement sparse outcome signals. We also construct RecruitmentBench, a recruitment persuasion dialogue benchmark grounded in anonymized real-world logs. Experiments on RecruitmentBench and two public datasets use offline real-log-grounded and cross-simulator protocols to evaluate intrinsic simulator consistency and downstream simulator-assisted policy performance. Results indicate that DPS improves measured simulator fidelity and yields stronger fine-tuned policy performance in these controlled evaluation settings, while remaining competitive with strong prompt-based LLM agents. We further calibrate our LLM-based evaluator against human judgments on MATH-CHAT. Code is available at: https://github.com/TraceLite-AI/DPS.

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.