Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline

ArXi:2605.06583v1 Announce Type: new We propose a deterministic adjoint matching framework that formulates human preference alignment for flow-based generative models as an optimal control problem over velocity fields. One can directly regress the control toward a value-gradient-induced target under the current policy, leading to a simple and stable