aiqbench analysis
This research introduces StagePilot, a reinforcement learning framework designed to simulate long-horizon dialogue trajectories for modeling cybergrooming scenarios. By decoupling stage-level planning from response generation, the system achieves more coherent progression while maintaining ethical constraints on emotional alignment. The work represents a significant advance in applying advanced machine learning techniques to safety-critical social simulation tasks.
StagePilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergrooming
arXiv:2602.05060v2 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2602.05060 [cs.LG]
(or arXiv:2602.05060v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.05060 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 218 words
