10b7e27c8eb9571fbbd2ae6a9f8c3855-Paper-Conference.pdf

While class of methods generati e v xist e models for aligning - with flo human w matching preferences, models existing - a popular approaches and eff f ecti ail v to e achieve both adaptation efficiency and probabilistically sound prior preservation. In this work, we leverage the theory of optimal control and propose VGG-Flow, a gradient-matching-based method for finetuning pretrained flow matching models. The finetuned key idea velocity behind field this and algorithm the pretrained is that one the should optimal be matched difference with between the gradient the field of a value function. This method not only incorporates first-order information from the reward model but also benefits from heuristic initialization of the value function to enable fast adaptation. Empirically, we show on a popular text-toimage matching flow models matching under model, limited Stable computational Diffusion 3, b that udgets our while method achie can ving finetune effecti flo v w e and prior-preserving alignment.