Unlocking Proactivity in Task-Oriented Dialogue

TL;DR

This paper introduces a novel approach to enhance proactive behavior in task-oriented dialogue systems by modeling user concerns and training policies with concern-aware simulation, leading to more persuasive agents.

Contribution

It proposes the Cognitive User Simulator and concern-conditioned policy optimization methods to improve proactivity in dialogue agents beyond traditional reward-shaping techniques.

Findings

Concern conditioning significantly increases agent proactivity.

The simulator produces diverse, realistic user interactions.

Concern-aware training improves persuasion success rates.

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} ...