DeFlow: Decoupling Behavior-Prior Modeling and Value Maximization for Offline Policy Extraction
DeFlow is an offline RL framework that separates behavior modeling from value maximization using flow matching for behavior cloning and a lightweight refinement module for Q-value optimization. It avoids difficult ODE solver differentiation by learning a residual action shift within a data-derived trust region, leading to stable policy extraction and superior performance on complex benchmarks.