Byte Seed reorganization: pre-training, RL consolidation, separate groups of office agents

Comparative news, AI news, ByteDance Big Model Team Seed completed a new round of organizational adjustments. Seed Foundation Model established four new first-level departments to re-merge data and post-training teams previously scattered in the fields of text, code, vision, and speech. The core change is to unify pre-training data with reinforcement learning, and at the same time split post-application training into two lines of Work and Chat. Pretrain Data unifies the multi-modal data for Omni models and the data required for pre-training very large models. Horizon RL focuses on strengthening learning and improving basic model capabilities. Product Posttrain-Work is aimed at the office and B-side, and focuses on optimizing the ability to call models, operate computers, and perform long-term tasks. The original Application team changed its name to Product Posttrain-Chat and continues to be responsible for the C-side conversation model. All four departments reported to Wu Yonghui. In the past, Seed was more divided into teams based on text, code, vision, voice, etc., and each had data and post-training personnel. Now Byte wants the next generation model to go directly to Omni, so that different modes enter the same base. Seed was also previously discussing training models with more than 5 trillion parameters. The larger the model and the more modes, the easier it is for the original scattered R&D methods to cause repeated investment. Similar adjustments have also appeared on Tencent. Tencent merged the mixed big language model and multi-modal team in July, with Yao Shunyu under unified responsibility. Byte also set up a special post-work training department this time, indicating that Office Agent has become a product line optimized separately by the basic model team.