Unlocking Proactivity in Task-Oriented Dialogue

• URL: http://arxiv.org/abs/2605.22240v1
• Date: Thu, 21 May 2026 09:46:25 GMT
• Title: Unlocking Proactivity in Task-Oriented Dialogue
• Abstract summary: Proactive task-oriented dialogue (TOD) demands a persuasive agent that actively probes the user's concerns and steers the conversation toward acceptance.<n>We show that conditioning on the user's latent concerns unlocks proactive capability that no amount of sampling can undermine.
• Score: 21.99795860301162
• License: http://creativecommons.org/licenses/by/4.0/
• 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} ...

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