Q-Learning with Scalar Adjoint Matching

Scalar adjoint matching accelerates flow policy fine-tuning by approximating the velocity Jacobian as a diagonal matrix. This method bypasses expensive vector-Jacobian products across flow steps, enabling efficient off-policy RL updates. Practitioners can now scale value-based refinement to larger models and longer diffusion chains without linear computational overhead.