RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social
  Networking Services
As a key medium for human interaction and information exchange, social
networking services (SNS) pose unique challenges for large language models
(LLMs): heterogeneous workloads, fast-shifting norms and slang, and
multilingual, culturally diverse corpora that induce sharp distribution shift.
Supervised fine-tuning (SFT) can specialize models but often triggers a
``seesaw'' between in-distribution gains and out-of-distribution robustness,
especially for smaller models. To address these challenges, we introduce RedOne
2.0, an SNS-oriented LLM trained with a progressive, RL-prioritized
post-training paradigm designed for rapid and stable adaptation. The pipeline
consist in three stages: (1) Exploratory Learning on curated SNS corpora to
establish initial alignment and identify systematic weaknesses; (2) Targeted
Fine-Tuning that selectively applies SFT to the diagnosed gaps while mixing a
small fraction of general data to mitigate forgetting; and (3) Refinement
Learning that re-applies RL with SNS-centric signals to consolidate
improvements and harmonize trade-offs across tasks. Across various tasks
spanning three categories, our 4B scale model delivers an average improvements
about 2.41 over the 7B sub-optimal baseline. Additionally, RedOne 2.0 achieves
average performance lift about 8.74 from the base model with less than half the
data required by SFT-centric method RedOne, evidencing superior data efficiency
and stability at compact scales. Overall, RedOne 2.0 establishes a competitive,
cost-effective baseline for domain-specific LLMs in SNS scenario, advancing
capability without sacrificing robustness.
6912afc3a644ba07c499c72b
Fei Zhao
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2026-02-02T17:03:10.029Z
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Fei Zhao
Hiiamein
user
Hiiamein
6912afc3a644ba07c499c72c
Chonggang Lu
6912afc3a644ba07c499c72d
Haofu Qian
6912afc3a644ba07c499c72e
Fangcheng Shi
6912afc3a644ba07c499c72f
Zijie Meng
6912afc3a644ba07c499c730
Jianzhao Huang
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Xu Tang
6912afc3a644ba07c499c732
Zheyong Xie
6912afc3a644ba07c499c733
Zheyu Ye
6912afc3a644ba07c499c734
Zhe Xu
6912afc3a644ba07c499c735
Yao Hu
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Shaosheng Cao
RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social
  Networking Services
As a key medium for human interaction and information exchange, social
networking services (SNS) pose unique challenges for large language models
(LLMs): heterogeneous workloads, fast-shifting norms and slang, and
multilingual, culturally diverse corpora that induce sharp distribution shift.
Supervised fine-tuning (SFT) can specialize models but often triggers a
``seesaw'' between in-distribution gains and out-of-distribution robustness,
especially for smaller models. To address these challenges, we introduce RedOne
2.0, an SNS-oriented LLM trained with a progressive, RL-prioritized
post-training paradigm designed for rapid and stable adaptation. The pipeline
consist in three stages: (1) Exploratory Learning on curated SNS corpora to
establish initial alignment and identify systematic weaknesses; (2) Targeted
Fine-Tuning that selectively applies SFT to the diagnosed gaps while mixing a
small fraction of general data to mitigate forgetting; and (3) Refinement
Learning that re-applies RL with SNS-centric signals to consolidate
improvements and harmonize trade-offs across tasks. Across various tasks
spanning three categories, our 4B scale model delivers an average improvements
about 2.41 over the 7B sub-optimal baseline. Additionally, RedOne 2.0 achieves
average performance lift about 8.74 from the base model with less than half the
data required by SFT-centric method RedOne, evidencing superior data efficiency
and stability at compact scales. Overall, RedOne 2.0 establishes a competitive,
cost-effective baseline for domain-specific LLMs in SNS scenario, advancing
capability without sacrificing robustness.
RedOne 2.0, a social networking service-oriented LLM, uses a progressive, RL-prioritized post-training paradigm to achieve rapid and stable adaptation, delivering improvements over larger baselines with less data.
supervised fine-tuning RL-prioritized exploratory learning targeted fine-tuning refinement learning domain-specific LLMs