Mo-Kai Pan

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 propose QAMM, a meth...