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The paper proposes Fixed-point Flow Matching (FFM), a data-free method for training and fine-tuning flow-based policies in online RL by formulating the actor update as a fixed-point iteration where the critic's action gradients serve as flow-matching regression targets, showing effective offline-to-online fine-tuning and from-scratch training.