RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services

TL;DR

RedOne 2.0 introduces a progressive RL-prioritized post-training approach for social networking services-oriented LLMs, achieving significant performance gains, data efficiency, and robustness at a smaller scale.

Contribution

The paper presents a novel three-stage post-training paradigm for SNS-specific LLMs, combining exploratory learning, targeted fine-tuning, and reinforcement learning for improved adaptation.

Findings

Achieves 2.41 average performance improvement over a 7B baseline.

Attains 8.74 average performance lift with less than half the data.

Demonstrates superior data efficiency and robustness at 4B scale.

Abstract

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.