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R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning

Paper · 2026 · Minggui He, Yilun Liu, Shimin Tao et al.

Summary

Reasoning-oriented LLMs that think step by step before answering have mostly been applied to math and code. Machine translation has received less attention, even though professional translators reason in layers. Earlier reasoning-based MT work either used one fixed thinking procedure (e.g., TASTE) or LLM-distilled chains of thought (e.g., DRT, aimed at Chinese-English literary translation), trained only with SFT. That setup tends to overfit and generalizes poorly. R1-Translator (R1-T1) addresses this in two stages:

  1. Cold start with SFT. Working with professional translators, the authors defined six CoT templates that reflect common translator strategies, such as context-aware paraphrasing and round-trip translation. They then fine-tuned the model on a mix of these reasoning trajectories.
  2. RL with MT-specific rewards. This stage lets the model adapt the learned strategies flexibly rather than reciting them.

Both human and automatic evaluation show consistent quality gains across 10+ languages and 40+ directions on Flores-101 and four domain-specific tasks. The gains are largest for languages unseen in training. This suggests that human-aligned reasoning plus RL is a general way to improve LLM translation, not one limited to a single domain.

Key points

  • R1-T1 relies on six expert-curated CoT templates covering hybrid translator strategies, including round-trip translation and context-aware paraphrasing.
  • Training has two stages: SFT on hybrid CoT trajectories as a cold start, then RL refinement with MT-tailored rewards. One shown configuration uses Qwen2.5-7B-Instruct as the base model.
  • Evaluation covers Flores-101 (10+ languages, 40+ directions, including low-resource ones) and four domain MT tasks, such as culture, common sense and terminology-constrained translation.
  • In ablation case studies, SFT-only reproduces strategies as a rigid narrative, while RL-only yields shallow reasoning and omissions. The combined model grounds its reasoning decisions in the final output.
  • Baselines in the case studies include DeepSeek-R1-Distill-Qwen-7B and Marco-o1. R1-T1 avoids their verbose, unresolved reasoning and the omissions in their outputs.

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

🏢Huawei TechnologiesShenzhen · China96 works · 15,432 citations🎓Xiamen UniversityXiamen · China24 works · 2,317 citations🎓Waseda UniversityTokyo · Japan21 works · 584 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,685 works

Cite

Cite as Minggui He, Yilun Liu, Shimin Tao, Hongyong Zeng, Jian Zhang, Yuanchang Luo, Li Zhang, Daimeng Wei, Weibin Meng and Osamu Yoshie. 2026. R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl.a.793

@article{he2026r1,
  title = {{R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning}},
  author = {Minggui He, Yilun Liu, Shimin Tao, Hongyong Zeng, Jian Zhang, Yuanchang Luo, Li Zhang, Daimeng Wei, Weibin Meng and Osamu Yoshie},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2026},
  doi = {10.1162/tacl.a.793},
  url = {https://doi.org/10.1162/tacl.a.793}
}

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