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QAMM: Adjoint MeanFlow Matching for Few-Step Offline Reinforcement Learning
Flow policies can model rich action distributions, but their iterative sampling limits decision speed. Adjoint matching uses the critic's action gradient to improve a flow policy without backpropagating through its sampling trajectory, yet its supervision is defined for instantaneous velocities. We
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