Reinforcement Learning for Adaptive User Preference Optimization in Task-Oriented Dialogue Systems
Keywords:
reinforcement learning; task-oriented dialogue systems; preference optimization; human feedback; governance; robustness; fairnessAbstract
Task-oriented dialogue systems are increasingly expected to optimize interaction outcomes beyond fixed slot-filling and intent classification. Reinforcement learning provides a principled framework for treating user preference as a nonstationary signal that emerges through repeated interaction. This paper examines the integration of reinforcement learning for adaptive user preference optimization in task-oriented dialogue systems from a systems perspective. It considers architectural trade-offs, reward modeling, safety constraints, data infrastructure, deployment, robustness, fairness, and governance. Rather than presenting a single algorithm, the paper argues that sustainable preference optimization requires a layered architecture in which preference elicitation, policy adaptation, and evaluation operate under explicit policy and resource constraints. The discussion highlights the tension between short-term task completion and long-term user satisfaction, the risks of reward hacking, distributional shift, and bias amplification, and the need for continuous monitoring in production environments. Case illustrations from customer service, healthcare scheduling, and smart assistant deployments are used to compare design choices. The paper further reviews how pairwise preference models and dual-stage transformer architectures support scalable preference estimation within constrained deployment environments. It concludes with policy implications for auditability, human oversight, and institutional governance, emphasizing that reinforcement learning in conversational systems should be treated as a socio-technical infrastructure rather than an isolated optimization problem.
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