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

• URL: http://arxiv.org/abs/2507.10605v2
• Date: Sun, 12 Oct 2025 08:15:51 GMT
• Title: RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services
• Authors: 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,
• Abstract summary: We introduce RedOne, a domain-specific language model for social networking services (SNS)<n>RedOne was developed through a three-stage training strategy consisting of continue pretraining, supervised fine-tuning, and preference optimization.<n>It achieves an average improvement up to 14.02% across 8 major SNS tasks and 7.56% in SNS bilingual evaluation benchmark.
• Score: 37.76677833724781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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