Human-in-the-Loop Reinforcement Learning for Next-Generation Adaptive Interfaces

Abstract

Most adaptive interface research lands in one of two places: fully automated systems that ignore the user's evolving intent, or static personalization that freezes in place after an initial configuration pass. Neither age well. This paper takes a different approach. We propose a Human-in-the-Loop Reinforcement Learning (HITL-RL) framework where the interface policy continues to evolve through a combination of behavioral observation and direct user input, without requiring the user to become an unwitting trainer. The core architecture pairs a reinforcement learning agent with a hybrid feedback channel that fuses implicit signals (click patterns, dwell time, abandonment) with explicit corrections users make. Drawing on reward modeling techniques from large-scale language model alignment work, we adapt the preference learning paradigm to interface-level decisions: layout, component prioritization, and interaction sequencing. Policy updates run continuously but remain interpretable, giving users meaningful visibility into why the interface changed. We evaluate the framework against static personalization baselines and fully automated RL approaches across three interface contexts. The results show measurably faster convergence to preferred layouts, higher rated usability scores, and lower correction frequency over time, which suggests the system is learning rather than just adapting noisily. Beyond the metrics, HITL-RL shifts the design philosophy: the interface is not a product delivered to the user, it is a policy negotiated with them.