TuneJury: An Open Metric for Improving Music Generation Preference Alignment
We introduce TuneJury, an open, instance-level pairwise reward model for text-to-music that predicts a music preference score from a text prompt and an audio clip. The released checkpoint is trained on publicly available human-preference labels covering arena-style (A vs. B) votes, metric-alignment preference pairs, crowdsourced pairwise comparisons, and expert aesthetic ratings. The predicted score margin between two clips is well calibrated on our held-out test split, supporting data filtering via a simple score threshold. TuneJury generalizes to both held-out test pairs and out-of-distribution benchmarks, remaining competitive with prior baselines on the latter. For generators released...
| Authors | Yonghyun Kim, Junwon Lee, Haiwen Xia et al. |
|---|---|
| Primary category | cs.SD |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
Models and datasets referencing this paper
- TuneJury/tunejuryModel
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