We present Distributional RewArds for Generative OptimizatioN (DRAGON), a
versatile framework for fine-tuning media generation models towards a desired
outcome. Compared with traditional reinforcement learning with human feedback
(RLHF) or pairwise preference approaches such as direct preference optimization
(DPO), DRAGON is more flexible. It can optimize reward functions that evaluate
either individual examples or distributions of them, making it compatible with
a broad spectrum of instance-wise, instance-to-distribution, and
distribution-to-distribution rewards. Leveraging this versatility, we construct
novel reward functions by selecting an encoder and a set of reference examples
to create an exemplar distribution. When cross-modality encoders such as CLAP
are used, the reference examples may be of a different modality (e.g., text
versus audio). Then, DRAGON gathers online and on-policy generations, scores
them to construct a positive demonstration set and a negative set, and
leverages the contrast between the two sets to maximize the reward. For
evaluation, we fine-tune an audio-domain text-to-music diffusion model with 20
different reward functions, including a custom music aesthetics model, CLAP
score, Vendi diversity, and Frechet audio distance (FAD). We further compare
instance-wise (per-song) and full-dataset FAD settings while ablating multiple
FAD encoders and reference sets. Over all 20 target rewards, DRAGON achieves an
81.45% average win rate. Moreover, reward functions based on exemplar sets
indeed enhance generations and are comparable to model-based rewards. With an
appropriate exemplar set, DRAGON achieves a 60.95% human-voted music quality
win rate without training on human preference annotations. As such, DRAGON
exhibits a new approach to designing and optimizing reward functions for
improving human-perceived quality. Sound examples at
https://ml-dragon.github.io/web.