[2604.05125] Offline RL for Adaptive Policy Retrieval in Prior Authorization

Computer Science > Information Retrieval arXiv:2604.05125 (cs) [Submitted on 6 Apr 2026] Title:Offline RL for Adaptive Policy Retrieval in Prior Authorization Authors:Ruslan Sharifullin, Maxim Gorshkov, Hannah Clay View a PDF of the paper titled Offline RL for Adaptive Policy Retrieval in Prior Authorization, by Ruslan Sharifullin and 2 other authors View PDF HTML (experimental) Abstract:Prior authorization (PA) requires interpretation of complex and fragmented coverage policies, yet existing retrieval-augmented systems rely on static top-$K$ strategies with fixed numbers of retrieved sections. Such fixed retrieval can be inefficient and gather irrelevant or insufficient information. We model policy retrieval for PA as a sequential decision-making problem, formulating adaptive retrieval as a Markov Decision Process (MDP). In our system, an agent iteratively selects policy chunks from a top-$K$ candidate set or chooses to stop and issue a decision. The reward balances decision correctness against retrieval cost, capturing the trade-off between accuracy and efficiency. We train policies using Conservative Q-Learning (CQL), Implicit Q-Learning (IQL), and Direct Preference Optimization (DPO) in an offline RL setting on logged trajectories generated from baseline retrieval strategies over synthetic PA requests derived from publicly available CMS coverage data. On a corpus of 186 policy chunks spanning 10 CMS procedures, CQL achieves 92% decision accuracy (+30 percentage points ...