Reinforcement Learning for Personalized Dialogue Generation in Intelligent Assistants
Keywords:
personalized dialogue generation, reinforcement learning, intelligent assistants, alignment, sociotechnical systems, system governanceAbstract
Personalized dialogue generation has become a central requirement for intelligent assistants operating across heterogeneous user populations, interaction contexts, and service ecosystems. Reinforcement learning offers a principled framework for optimizing dialogue policies from delayed and subjective feedback, but its integration into assistive systems introduces substantial structural, infrastructural, and governance challenges that cannot be reduced to model accuracy alone. This paper provides a system-level examination of reinforcement learning for personalized dialogue generation, focusing on architectural trade-offs, reward design, deployment constraints, robustness, fairness, sustainability, and regulatory implications. It argues that personalization must be understood as a sociotechnical process rather than as a purely statistical adaptation problem. The discussion examines how policy optimization techniques can be combined with human preference signals to align assistant behavior with user intentions while also creating risks related to reward misspecification, conversational overfitting, privacy leakage, and distributional bias. The paper further considers the energy and computational demands of iterative personalization, the difficulty of maintaining stable behavior under online learning, and the need for governance structures that can address value pluralism across institutional and cultural settings. By connecting reinforcement learning formalisms with deployment realities, the paper offers a forward-looking analysis of how personalized dialogue systems can be designed, evaluated, and regulated in ways that are technically sound, socially legible, and institutionally robust.
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