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

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ethics.ai (15 June 2026), “TuneJury: An Open Metric for Improving Music Generation Preference Alignment,” evidence record 942, https://ethics.ai/record/942 (originally published by arXiv).

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