  We have made significant progress towards building foundational video
diffusion models. As these models are trained using large-scale unsupervised
data, it has become crucial to adapt these models to specific downstream tasks.
Adapting these models via supervised fine-tuning requires collecting target
datasets of videos, which is challenging and tedious. In this work, we utilize
pre-trained reward models that are learned via preferences on top of powerful
vision discriminative models to adapt video diffusion models. These models
contain dense gradient information with respect to generated RGB pixels, which
is critical to efficient learning in complex search spaces, such as videos. We
show that backpropagating gradients from these reward models to a video
diffusion model can allow for compute and sample efficient alignment of the
video diffusion model. We show results across a variety of reward models and
video diffusion models, demonstrating that our approach can learn much more
efficiently in terms of reward queries and computation than prior gradient-free
approaches. Our code, model weights,and more visualization are available at
https://vader-vid.github.io.
