AI RESEARCH

SPAR: Support-Preserving Action Rectification

arXiv CS.AI

ArXi:2605.27877v1 Announce Type: cross Offline policy improvement faces an inherent conflict between maximizing value and fitting the data distribution. While in-sample weighted regression is stable, it suffers from over-conservatism that suppresses high-value actions in the distribution tail; conversely, gradient-based approaches often exhibit a fitting-optimization conflict of gradients, which drives the policy off the data manifold.