AI RESEARCH
Entropy-Regularized Adjoint Matching for Offline RL
arXiv CS.LG
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ArXi:2605.06156v1 Announce Type: new Integrating expressive generative policies, such as flow-matching models, into offline reinforcement learning (RL) allows agents to capture complex, multi-modal behaviors. While Q-learning with Adjoint Matching (QAM) stabilizes policy optimization via the continuous adjoint method, it remains inherently bound to the fixed behavior distribution. This dependence induces a \textit{popularity bias} that can suppress high-reward actions in low-density regions, and creates a \textit{ binding} that restricts off-manifold exploration.