R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning
  Learning
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.
67c1438fd7ffcd1cab1fc412
Minggui He
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2025-02-28T05:03:12.675Z
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he
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Yilun Liu
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2025-03-04T08:52:32.981Z
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Yilun Liu
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lunyiliu
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Shimin Tao
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Yuanchang Luo
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Hongyong Zeng
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Chang Su
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Li Zhang
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Hongxia Ma
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Daimeng Wei
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Weibin Meng
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2025-02-28T12:44:36.712Z
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WeibinMeng
user
weibinmeng
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Hao Yang
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Boxing Chen
admin_assigned
2025-02-28T12:44:51.085Z
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Boxing Chen
user
BoxingChen
67c1438fd7ffcd1cab1fc41e
Osamu Yoshie
R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning
  Learning
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.
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.
reasoning-enhanced large language models machine translation chain-of-thoughts inference-time reasoning reinforcement learning KL-constrained rewards