A Diagnostic Framework for Auditing Validation-Driven Adaptive Reward Weighting in Molecular Generation
This paper introduces a rigorous diagnostic framework for auditing adaptive reward weighting in molecular generation that, when applied to a locked REINVENT4 experiment, reveals that the tested controller failed to demonstrate practically meaningful improvements over static baselines despite appearing successful in development, thereby establishing a reusable template for separating genuine signal from temporal noise and oracle artifacts.