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.

翻译：网络诱骗是对青少年日益增长的威胁，需要主动的教育干预。我们通过将对话进程建模为阶段式交互上的结构化规划问题来应对这一挑战。我们提出StagePilot对话框架，将阶段级规划与响应生成分离，模型在受约束的转移条件下选择下一阶段，并基于此生成响应，从而产生连贯且真实的进程。利用强化学习从离线数据中学习阶段级策略，优化情感对齐与目标一致进程。我们的实验表明，相比基线方法，StagePilot能生成更具结构化、更连贯的对话轨迹，减少对话停滞；值得注意的是，IQL+AWAC变体在更频繁到达最终阶段的同时，维持超过70%的积极或中立响应，实现了43%的相对改进。