title: R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning
    Learning
summary: Despite recent breakthroughs in reasoning-enhanced large language models
  (LLMs) like DeepSeek-R1, incorporating inference-time reasoning into machine
  translation (MT), where human translators naturally employ structured,
  multi-layered reasoning chain-of-thoughts (CoTs), is yet underexplored.
  Existing methods either design a fixed CoT tailored for a specific MT sub-task
  (e.g., literature translation), or rely on synthesizing CoTs unaligned with
  humans and supervised fine-tuning (SFT) prone to catastrophic forgetting,
  limiting their adaptability to diverse translation scenarios. This paper
  introduces R1-Translator (R1-T1), a novel framework to achieve inference-time
  reasoning for general MT via reinforcement learning (RL) with human-aligned
  CoTs comprising six common patterns. Our approach pioneers three innovations:
  (1) extending reasoning-based translation beyond MT sub-tasks to six languages
  and diverse tasks (e.g., legal/medical domain adaptation, idiom resolution);
  (2) formalizing six expert-curated CoT templates that mirror hybrid human
  strategies like context-aware paraphrasing and back translation; and (3)
  enabling self-evolving CoT discovery and anti-forgetting adaptation through RL
  with KL-constrained rewards. Experimental results indicate a steady translation
  performance improvement in 21 languages and 80 translation directions on
  Flores-101 test set, especially on the 15 languages unseen from training, with
  its general multilingual abilities preserved compared with plain SFT.
paper_authors._id: 67c1438fd7ffcd1cab1fc412
paper_authors.name: Minggui He
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-02-28T05:03:12.675Z
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paper_authors.fullname: he
paper_authors.type: user
paper_authors.user: boommmmm
paper_authors._id: 67c1438fd7ffcd1cab1fc413
paper_authors.name: Yilun Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T08:52:32.981Z
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paper_authors.avatarUrl: /avatars/3bbfd9fd20b2e6f9dd40c2fc7f74e241.svg
paper_authors.fullname: Yilun Liu
paper_authors.type: user
paper_authors.user: lunyiliu
paper_authors._id: 67c1438fd7ffcd1cab1fc414
paper_authors.name: Shimin Tao
paper_authors._id: 67c1438fd7ffcd1cab1fc415
paper_authors.name: Yuanchang Luo
paper_authors._id: 67c1438fd7ffcd1cab1fc416
paper_authors.name: Hongyong Zeng
paper_authors._id: 67c1438fd7ffcd1cab1fc417
paper_authors.name: Chang Su
paper_authors._id: 67c1438fd7ffcd1cab1fc418
paper_authors.name: Li Zhang
paper_authors._id: 67c1438fd7ffcd1cab1fc419
paper_authors.name: Hongxia Ma
paper_authors._id: 67c1438fd7ffcd1cab1fc41a
paper_authors.name: Daimeng Wei
paper_authors._id: 67c1438fd7ffcd1cab1fc41b
paper_authors.name: Weibin Meng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T12:44:36.712Z
paper_authors._id: 67285a87e347d62d66473b9a
paper_authors.avatarUrl: /avatars/6aeb022c2728ace62bf6884fdb3c9f9c.svg
paper_authors.fullname: WeibinMeng
paper_authors.type: user
paper_authors.user: weibinmeng
paper_authors._id: 67c1438fd7ffcd1cab1fc41c
paper_authors.name: Hao Yang
paper_authors._id: 67c1438fd7ffcd1cab1fc41d
paper_authors.name: Boxing Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T12:44:51.085Z
paper_authors._id: 66f98351ee969ff116986327
paper_authors.avatarUrl: /avatars/e038ef1462f77f5e87e868339993f92d.svg
paper_authors.fullname: Boxing Chen
paper_authors.type: user
paper_authors.user: BoxingChen
paper_authors._id: 67c1438fd7ffcd1cab1fc41e
paper_authors.name: Osamu Yoshie
paper_title: R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning
    Learning
paper_summary: Despite recent breakthroughs in reasoning-enhanced large language models
  (LLMs) like DeepSeek-R1, incorporating inference-time reasoning into machine
  translation (MT), where human translators naturally employ structured,
  multi-layered reasoning chain-of-thoughts (CoTs), is yet underexplored.
  Existing methods either design a fixed CoT tailored for a specific MT sub-task
  (e.g., literature translation), or rely on synthesizing CoTs unaligned with
  humans and supervised fine-tuning (SFT) prone to catastrophic forgetting,
  limiting their adaptability to diverse translation scenarios. This paper
  introduces R1-Translator (R1-T1), a novel framework to achieve inference-time
  reasoning for general MT via reinforcement learning (RL) with human-aligned
  CoTs comprising six common patterns. Our approach pioneers three innovations:
  (1) extending reasoning-based translation beyond MT sub-tasks to six languages
  and diverse tasks (e.g., legal/medical domain adaptation, idiom resolution);
  (2) formalizing six expert-curated CoT templates that mirror hybrid human
  strategies like context-aware paraphrasing and back translation; and (3)
  enabling self-evolving CoT discovery and anti-forgetting adaptation through RL
  with KL-constrained rewards. Experimental results indicate a steady translation
  performance improvement in 21 languages and 80 translation directions on
  Flores-101 test set, especially on the 15 languages unseen from training, with
  its general multilingual abilities preserved compared with plain SFT.
paper_ai_summary: A novel framework, R1-Translator, uses reinforcement learning with human-aligned chain-of-thoughts to enhance reasoning in machine translation across diverse languages and tasks.
paper_ai_keywords: reasoning-enhanced large language models machine translation chain-of-thoughts inference-time reasoning reinforcement learning KL-constrained rewards