Advantage-Conditioned Flow Policy for Offline Reinforcement Learning in Recommendation
PerfRec (Preference-aware Flow for Recommendation), a flow-matching offline RL framework that learns an expressive behavioral policy and distills it into an efficient one-step policy, is proposed and experiments show that PerfRec improves recommendation performance over strong offline RL baselines.