Improving Text-to-Music Generation with Human Preference Rewards
We describe our entry to the efficiency track of the Academic Text-to-Music (ATTM) Grand Challenge at ICME 2026. Beyond the challenge protocol's FAD-CLAP and CLAP score, we add a learned human-preference reward from TuneJury, a twin pairwise ranker trained over open music-preference datasets. The reward serves both as a training-time conditioning signal and as a sample-selection criterion. The pipeline combines five engineering decisions on a 120M-parameter FluxAudio-S backbone, four at training time and one at inference: (i) training-time reward conditioning that doubles as an inference-time CFG axis, (ii) a sweep over five score-conditioning architectures, where training and inference u...
| Authors | Yonghyun Kim, Junwon Lee, Haiwen Xia et al. |
|---|---|
| Primary category | cs.SD |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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