RedOne：揭秘社交网络服务中领域特定大语言模型的微调实践

RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services

July 13, 2025

作者: Fei Zhao, Chonggang Lu, Yue Wang, Zheyong Xie, Ziyan Liu, Haofu Qian, JianZhao Huang, Fangcheng Shi, Zijie Meng, Hongcheng Guo, Mingqian He, Xinze Lyu, Yiming Lu, Ziyang Xiang, Zheyu Ye, Chengqiang Lu, Zhe Xu, Yi Wu, Yao Hu, Yan Gao, Jun Fan, Xiaolong Jiang, Weiting Liu, Boyang Wang, Shaosheng Cao

cs.AI

摘要

作为现代信息传播的主要媒介，社交网络服务（SNS）经历了迅猛发展，这为平台内容管理和互动质量提升带来了重大挑战。近期，大语言模型（LLMs）的发展提供了潜在的解决方案，但现有研究多集中于孤立任务，不仅面临单一场景下数据扩展效益递减的问题，还难以灵活适应多样化的现实情境。为应对这些挑战，我们推出了RedOne，一款专为SNS设计的领域特定大语言模型，旨在突破单任务基线的性能瓶颈，为SNS构建一个全面的基础。RedOne通过包含持续预训练、监督微调和偏好优化的三阶段训练策略开发，并利用大规模真实世界数据集。经过广泛实验，RedOne不仅保持了强大的通用能力，还在8项主要SNS任务上平均提升达14.02%，在SNS双语评估基准上提升7.56%，相较于基础模型。此外，在线测试显示，RedOne在有害内容检测中的曝光率降低了11.23%，在帖子浏览搜索中的点击页面率提升了14.95%，相较于单任务微调的基线模型。这些成果确立了RedOne作为SNS领域特定大语言模型的强大地位，展现了其在多样化任务中的卓越泛化能力及在现实场景中的广阔应用前景。

English

As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. Recently, the development of large language models (LLMs) has offered potential solutions but existing studies focus on isolated tasks, which not only encounter diminishing benefit from the data scaling within individual scenarios but also fail to flexibly adapt to diverse real-world context. To address these challenges, we introduce RedOne, a domain-specific LLM designed to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for the SNS. RedOne was developed through a three-stage training strategy consisting of continue pretraining, supervised fine-tuning, and preference optimization, using a large-scale real-world dataset. Through extensive experiments, RedOne maintains strong general capabilities, and achieves an average improvement up to 14.02% across 8 major SNS tasks and 7.56% in SNS bilingual evaluation benchmark, compared with base models. Furthermore, through online testing, RedOne reduced the exposure rate in harmful content detection by 11.23% and improved the click page rate in post-view search by 14.95% compared with single-tasks finetuned baseline models. These results establish RedOne as a robust domain-specific LLM for SNS, demonstrating excellent generalization across various tasks and promising applicability in real-world scenarios.