RedOne: Revealing Domain-specific LLM Post-Training in Social Networking
  Services
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.
687da15e2e8db0930be6f0aa
Fei Zhao
claimed_verified
2025-07-21T08:29:35.346Z
65328aa39326d6da5ff19b52
/avatars/5c3de984cd6eba69616bb608796865c5.svg
Fei Zhao
user
Hiiamein
687da15e2e8db0930be6f0ab
Chonggang Lu
admin_assigned
2025-07-21T16:03:01.123Z
62ddf82126b500df9128cd04
/avatars/a23d5630e60f15d5e7ea8bdf6588d3af.svg
Chonggang Lu
user
BurgerBurger
687da15e2e8db0930be6f0ac
Yue Wang
687da15e2e8db0930be6f0ad
Zheyong Xie
687da15e2e8db0930be6f0ae
Ziyan Liu
687da15e2e8db0930be6f0af
Haofu Qian
687da15e2e8db0930be6f0b0
JianZhao Huang
admin_assigned
2025-07-21T16:03:33.720Z
64cfb5a10fbfb00b915f066c
/avatars/dbb6e24dc57517bfc3b53e466c40ba81.svg
Jianzhao Huang
user
Jianzhao12138
687da15e2e8db0930be6f0b1
Fangcheng Shi
687da15e2e8db0930be6f0b2
Zijie Meng
687da15e2e8db0930be6f0b3
Hongcheng Guo
687da15e2e8db0930be6f0b4
Mingqian He
687da15e2e8db0930be6f0b5
Xinze Lyu
687da15e2e8db0930be6f0b6
Yiming Lu
687da15e2e8db0930be6f0b7
Ziyang Xiang
687da15e2e8db0930be6f0b8
Zheyu Ye
687da15e2e8db0930be6f0b9
Chengqiang Lu
687da15e2e8db0930be6f0ba
Zhe Xu
687da15e2e8db0930be6f0bb
Yi Wu
687da15e2e8db0930be6f0bc
Yao Hu
687da15e2e8db0930be6f0bd
Yan Gao
687da15e2e8db0930be6f0be
Jun Fan
687da15e2e8db0930be6f0bf
Xiaolong Jiang
687da15e2e8db0930be6f0c0
Weiting Liu
687da15e2e8db0930be6f0c1
Boyang Wang
687da15e2e8db0930be6f0c2
Shaosheng Cao
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking
  Services
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.
RedOne, a domain-specific LLM, enhances performance across multiple SNS tasks through a three-stage training strategy, improving generalization and reducing harmful content exposure.
large language models domain-specific LLM continue pretraining supervised fine-tuning preference optimization harmful content detection post-view search