DITTO

TuneJury: Open pairwise reward model for text-to-music preference alignment

Researchers introduce TuneJury, an open-source instance-level pairwise reward model for text-to-music generation that predicts preference scores from text prompts and audio clips. The model is trained on publicly available human-preference labels spanning arena votes, crowdsourced comparisons, and expert ratings. A post-hoc anchor calibration method enables efficient adaptation to new generators without full retraining. The reward model drives gains across best-of-N selection, latent optimization, and expert-iteration post-training.