Preference-Shift Diagnostics for Iterative Policy Refinement
DOI:
https://doi.org/10.66857/jirt.v3i2.57Keywords:
Iterative Policy Refinement, Preference Dynamics, Diagnostic Frameworks, Reinforcement LearningAbstract
The deployment of autonomous systems in dynamic, human-centric environments necessitates continuous adaptation to evolving user preferences. Iterative policy refinement, a cornerstone of modern reinforcement learning and alignment strategies, often assumes stationary preference distributions. However, human preferences are inherently non-stationary, exhibiting complex temporal, contextual, and systemic shifts that degrade policy performance over time. This paper introduces a comprehensive framework for preference-shift diagnostics designed to identify, categorize, and address non-stationarity in iterative policy refinement pipelines. By establishing a robust diagnostic methodology, this research bridges the gap between static reward modeling and dynamic operational environments. We propose a multi-layered diagnostic architecture that continuously monitors feedback streams, detects distributional divergences, and attributes these shifts to specific environmental or human factors. Through extensive theoretical analysis and simulated experimental validation, we demonstrate that integrating preference-shift diagnostics significantly enhances the resilience and alignment of autonomous policies. The findings underscore the critical need for diagnostic transparency in policy optimization, providing a scalable solution for maintaining system efficacy in the face of shifting human expectations.
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