---
title: "RedOne 2.0 — paper by zhao et al. (2026) — summary and ideas"
description: "Xiaohongshu's NLP team proposes RedOne 2.0, which makes reinforcement learning the core of… 83 references, cited by 2 papers here."
authors: ["Fei zhao", "Chonggang Lu", "Haofu Qian", "Fangcheng Shi", "Zijie Meng", "Jianzhao Huang", "Zheyong Xie", "Shaosheng Cao"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)"
source: "https://aclanthology.org/2026.acl-industry.90/"
doi: "10.18653/v1/2026.acl-industry.90"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhao-2026-redone-2-0-rethinking-domain/"
---
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# RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services

**Authors:** [Fei zhao](https://md.research.elamur.ai/free/en/authors/fei-zhao/), Chonggang Lu, Haofu Qian, Fangcheng Shi, Zijie Meng, Jianzhao Huang, [Zheyong Xie](https://md.research.elamur.ai/free/en/authors/zheyong-xie/) and [Shaosheng Cao](https://md.research.elamur.ai/free/en/authors/shaosheng-cao/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-industry.90/) · [PDF](https://aclanthology.org/2026.acl-industry.90.pdf) · [DOI 10.18653/v1/2026.acl-industry.90](https://doi.org/10.18653/v1/2026.acl-industry.90) · [OpenAlex](https://openalex.org/W4416158743)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 2 in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLMs adapted to social networking services (SNS) face fast-changing slang, community norms, and culturally varied content. Supervised fine-tuning boosts in-domain skills but tends to cause a "seesaw" effect, where general robustness drops. This is especially true for small models. Xiaohongshu's NLP team proposes RedOne 2.0, which makes reinforcement learning the core of post-training through a three-step curriculum:

1. **Exploratory Learning.** DAPO-based RL on mixed SNS and general data, scored with task-appropriate rewards: exact match, metric-based, sandbox code execution, and pattern/constraint checks. This stage aligns the model and reveals weak tasks.
2. **Targeted Fine-Tuning.** SFT only on the diagnosed weak tasks, mixed with soft-labeled general data to limit forgetting.
3. **Refinement Learning.** A second RL pass that consolidates gains across tasks.

Evaluated on General-Bench, SNS-Bench, and SNS-TransBench, the compact 4B model outperforms the earlier 7B RedOne while using roughly half the training data. Online deployment on a large platform yielded measurable business gains. The work offers a data-efficient, cost-effective recipe for domain adaptation that preserves general capability.

## Key points
- **Training data:** The SNS data covers over 75 tasks across six capability areas.
  - Stage 1 uses 750K SNS samples plus 50K general samples.
  - Stage 2 uses 1.7M SNS samples plus 100K general samples. For the general part, the best of eight model-generated candidates, as judged by CompassJudger-2-32B-Instruct, becomes the soft label.
- **Benchmark gains:** RedOne 2.0 4B (built on Qwen3-4B) beats RedOne-7B by 2.41 points on average and its own base by 8.74, with under half the data of the SFT-centric approach.
- **Comparison with SFT followed by RL:** RedOne 2.0 leads by 1.00 on General-Bench, 4.54 on SNS-Bench, and 1.72 on SNS-TransBench, avoiding the general-domain trade-off. Unified multi-task training also beats task-specific SFT on MRC (+9.00) and CHLW (+11.87).
- **Online title-rewriting A/B test:** Advertiser Value rose by 0.43%. Vague titles fell by 11.9%, and interactive titles increased by 25.8%. Accuracy also improved in customer-service tasks: negative-attitude detection went from 90.45 to 96.18, and information-leakage detection from 71.39 to 94.85.
- **Limitations:**
  - The model is text-only, with no multimodal input.
  - Rewards are imperfect proxies for user satisfaction.
  - A reported failure case shows over-generalization that drops key factual details.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Xiaohongshu (Little Red Book)](https://md.research.elamur.ai/free/en/companies/xiaohongshu-little-red-book/) — Company, Shanghai, China · 11 works · 107 citations

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## Cite

Cite as: Fei zhao, Chonggang Lu, Haofu Qian, Fangcheng Shi, Zijie Meng, Jianzhao Huang, Zheyong Xie and Shaosheng Cao. 2026. RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhao2026redone,
  title = {{RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services}},
  author = {Fei zhao, Chonggang Lu, Haofu Qian, Fangcheng Shi, Zijie Meng, Jianzhao Huang, Zheyong Xie and Shaosheng Cao},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-industry.90},
  url = {https://doi.org/10.18653/v1/2026.acl-industry.90}
}
```

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*Updated 31 October 2026*