{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/eagle-2-building-post-training-data","title":"Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models","arxiv_id":"2501.14818","date":"2025-01-20","proceeding":null,"authors":["Zhiqi Li","Guo Chen","Shilong Liu","Shihao Wang","Vibashan VS","Yishen Ji","Shiyi Lan","Hao Zhang","Yilin Zhao","Subhashree Radhakrishnan","Nadine Chang","Karan Sapra","Amala Sanjay Deshmukh","Tuomas Rintamaki","Matthieu Le","Ilia Karmanov","Lukas Voegtle","Philipp Fischer","De-An Huang","Timo Roman","Tong Lu","Jose M. Alvarez","Bryan Catanzaro","Jan Kautz","Andrew Tao","Guilin Liu","Zhiding Yu"],"abstract":"Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models. However, most open-source models only publish their final model weights, leaving the critical details of data strategies and implementation largely opaque. In this work, we address VLM post-training from a data-centric perspective, showing the key role of data strategy in developing frontier VLMs. By studying and building our post-training data strategy from scratch, we share detailed insights into the development processes, aiming to benefit the development of competitive models for the open-source community. Our introduced data strategy, together with training recipes and model design, leads to a family of performant VLMs named Eagle2. 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