Poster Fri, Apr 24, 2026 • 6:30 AM – 9:00 AM PDT Pavilion 3 P3-#1813

YuE: Scaling Open Foundation Models for Long-Form Music Generation

Ruibin Yuan ⋅ Hanfeng Lin ⋅ Shuyue Guo ⋅ Ge Zhang ⋅ Jiahao Pan ⋅ Yongyi Zang ⋅ Haohe Liu ⋅ Yiming Liang ⋅ Wenye Ma ⋅ Xingjian Du ⋅ Xeron Du ⋅ Zhen Ye ⋅ Tianyu Zheng ⋅ Zhengxuan Jiang ⋅ Yinghao MA ⋅ Minghao Liu ⋅ Zeyue Tian ⋅ Ziya Zhou ⋅ Liumeng Xue ⋅ Xingwei Qu ⋅ Yizhi Li ⋅ Shangda Wu ⋅ Tianhao Shen ⋅ Ziyang Ma ⋅ Jun Zhan ⋅ Chunhui Wang ⋅ Yatian Wang ⋅ Xiaowei Chi ⋅ Xinyue Zhang ⋅ Zhenzhu Yang ⋅ XiangzhouWang ⋅ Shansong Liu ⋅ Lingrui Mei ⋅ Peng Li ⋅ JUNJIE WANG ⋅ Jianwei Yu ⋅ Guojian Pang ⋅ Xu Li ⋅ Zihao Wang ⋅ Xiaohuan Zhou ⋅ Lijun Yu ⋅ Emmanouil Benetos ⋅ Yong Chen ⋅ Chenghua Lin ⋅ Xie Chen ⋅ Gus Xia ⋅ Zhaoxiang Zhang ⋅ Chao Zhang ⋅ Wenhu Chen ⋅ Xinyu Zhou ⋅ Xipeng Qiu ⋅ Roger Dannenberg ⋅ JIAHENG LIU ⋅ Jian Yang ⋅ Wenhao Huang ⋅ Wei Xue ⋅ Xu Tan ⋅ Yike Guo

Abstract

We tackle the task of long-form music generation, particularly the challenging \textbf{lyrics-to-song} problem, by introducing \textbf{YuE (乐)}, a family of open-source music generation foundation models. Specifically, YuE scales to trillions of tokens and generates up to five minutes of music while maintaining lyrical alignment, coherent musical structure, and engaging vocal melodies with appropriate accompaniment. It achieves this through \textbf{track-decoupled next-token prediction} to overcome dense mixture signals, and \textbf{structural progressive conditioning} for long-context lyrical alignment. In addition, we redesign the \textbf{in-context learning} technique for music generation, enabling bidirectional content creation, style cloning, and improving musicality. Through extensive evaluation, we demonstrate that YuE matches or even surpasses some of the proprietary systems in musicality and vocal agility (as of 2025-01). We strongly encourage readers to \textbf{listen to our demo}\footnote{\url{https://map-yue.github.io/}}.