AINeutralarXiv – CS AI · Jun 46/10
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Unlocking Proactivity in Task-Oriented Dialogue
Researchers present a novel approach to training task-oriented dialogue agents that enables proactive behavior through a Cognitive User Simulator and asymmetric policy optimization. The method addresses a fundamental limitation in LLM-based dialogue systems by conditioning agent responses on modeled user concerns, achieving persuasive capabilities beyond what traditional reinforcement learning methods can accomplish.