{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:29:01Z","timestamp":1777631341711,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>We introduce NotaGen, a symbolic music generation model aiming to explore the potential of producing high-quality classical sheet music. Inspired by the success of Large Language Models (LLMs), NotaGen adopts pre-training, fine-tuning, and reinforcement learning paradigms (henceforth referred to as the LLM training paradigms). It is pre-trained on 1.6M pieces of music in ABC notation, and then fine-tuned on approximately 9K high-quality classical compositions conditioned on \"period-composer-instrumentation\"  prompts. For reinforcement learning, we propose the CLaMP-DPO method, which further enhances generation quality and controllability without requiring human annotations or predefined rewards. Our experiments demonstrate the efficacy of CLaMP-DPO in symbolic music generation models with different architectures and encoding schemes. Furthermore, subjective A\/B tests show that NotaGen outperforms baseline models against human compositions, greatly advancing musical aesthetics in symbolic music generation.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1134","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"10207-10215","source":"Crossref","is-referenced-by-count":10,"title":["NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms"],"prefix":"10.24963","author":[{"given":"Yashan","family":"Wang","sequence":"first","affiliation":[{"name":"Central Conservatory of Music, China"}]},{"given":"Shangda","family":"Wu","sequence":"additional","affiliation":[{"name":"Central Conservatory of Music, China"}]},{"given":"Jianhuai","family":"Hu","sequence":"additional","affiliation":[{"name":"Central Conservatory of Music, China"}]},{"given":"Xingjian","family":"Du","sequence":"additional","affiliation":[{"name":"University of Rochester, USA"}]},{"given":"Yueqi","family":"Peng","sequence":"additional","affiliation":[{"name":"Beijing Flowingtech Ltd., China"}]},{"given":"Yongxin","family":"Huang","sequence":"additional","affiliation":[{"name":"Independent Researcher"}]},{"given":"Shuai","family":"Fan","sequence":"additional","affiliation":[{"name":"Beihang University, China"}]},{"given":"Xiaobing","family":"Li","sequence":"additional","affiliation":[{"name":"Central Conservatory of Music, China"}]},{"given":"Feng","family":"Yu","sequence":"additional","affiliation":[{"name":"Central Conservatory of Music, China"}]},{"given":"Maosong","family":"Sun","sequence":"additional","affiliation":[{"name":"Central Conservatory of Music, China"},{"name":"Tsinghua University, China"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:14Z","timestamp":1758627374000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1134"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1134","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}