Abstract:Proactive task-oriented dialogue (TOD), such as outbound sales, demands a persuasive agent that actively probes the user's concerns and steers the conversation toward acceptance within a bounded number of turns. Yet post-trained LLMs are inherently conservative, and reward-shaping RL (e.g., GRPO) struggles since it only re-weights what an already passive policy samples. We show that conditioning on the user's latent concerns unlocks proactive capability that no amount of sampling can undermine, establishing these concerns as a pivotal training-time signal. To operationalize this finding, we build the \textbf{Cognitive User Simulator}, which models each user as a stratified persona comprising observable external traits and hidden internal concerns. The simulator produces faithful and diverse interactions, while emitting per-turn state dynamics that track persuasion progress. We then introduce \textbf{Simulator-Induced Asymmetric-View Policy Optimization}, which converts the modeled concerns and the simulation state transition into complementary training objectives: (1) \emph{Asymmetric On-Policy Self-Distillation} that transfers concern-aware behavior from a privileged view of the same policy into its deployable, conversation-only view; and (2) \emph{State-Transition Policy Refinement} ...
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.22240 [cs.AI] |
| (or arXiv:2605.22240v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22240 arXiv-issued DOI via DataCite (pending registration) |
From: Chaozheng Wang [view email]
[v1]
Thu, 21 May 2026 09:46:25 UTC (465 KB)
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