{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:09:15Z","timestamp":1773803355163,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"29","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Recent advances in large language models (LLMs) have driven impressive progress in omni-modal understanding and generation. However, training omni-modal LLMs remains a significant challenge due to the heterogeneous model architectures required to process diverse modalities, necessitating sophisticated system design for efficient large-scale training. Existing frameworks typically entangle model definition with parallel logic, incurring limited scalability and substantial engineering overhead for end-to-end omni-modal training. We present OmniScale, a modular and efficient training framework to accelerate the development of omni-modal LLMs. OmniScale introduces model-centric distributed recipes that decouples communication from computation, enabling efficient 3D parallelism on omni-modal LLMs. OmniScale also features a flexible configuration interface supporting seamless integration of new modalities with minimal code change. Using OmniScale, a omni-modal mixture-of-experts (MoE) model with 30B parameters can be trained with over 2,800 tokens\/sec\/GPU throughput and scale to 160K context lengths via 3D parallelism on 128 GPUs, showcasing its superior efficiency and scalability for training large omni-modal LLMs.<\/jats:p>","DOI":"10.1609\/aaai.v40i29.39607","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:51:08Z","timestamp":1773798668000},"page":"24271-24280","source":"Crossref","is-referenced-by-count":0,"title":["OmniScale: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo"],"prefix":"10.1609","volume":"40","author":[{"given":"Qianli","family":"Ma","sequence":"first","affiliation":[]},{"given":"Yaowei","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Zhelun","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Zhongkai","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Ziyue","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zhiqi","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Youjie","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jiacheng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yanghua","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Zhi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39607\/43568","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39607\/43568","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:51:08Z","timestamp":1773798668000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/39607"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"29","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i29.39607","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}