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

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arxiv 2502.19735 v3 pith:LHWQ7E3M submitted 2025-02-27 cs.CL

Minggui He , Yilun Liu , Shimin Tao , Yuanchang Luo , Hongyong Zeng , Chang Su , Li Zhang , Hongxia Ma
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Daimeng Wei Weibin Meng Hao Yang Boxing Chen Osamu Yoshie
This is my paper
classification cs.CL
keywords translationreasoningcotshumaninference-timelanguageslearninglike
verification ladder T0 review T1 audit T2 compute T3 formal
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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 overfitting, 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 to broader MT scenarios (e.g., multilingual MT, domain MT) unseen in the training phase; (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 through RL. Both human and automatic evaluation results indicate a steady translation performance improvement in a total of 10+ languages and 40+ translation directions on Flores-101 test set and four domain-specific MT tasks, especially on the languages unseen from training.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Price of Reasoning: Cost-Quality Tradeoffs in Reinforcement Learning for Neural Machine Translation

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Reasoning traces improve legal MT mainly when enabled at inference, and training with reasoning keeps those traces compact enough to be cost-effective.

  2. Chart Specification: Structural Representations for Incentivizing VLM Reasoning in Chart-to-Code Generation

    cs.CV 2026-02 conditional novelty 6.0 of 10

    A 7B VLM trained with a structured chart-specification reward beats larger and commercial models on chart-to-code benchmarks using only 3K-4K training samples.

  3. Seed LiveInterpret 2.0: End-to-end Simultaneous Speech-to-speech Translation with Your Voice

    cs.CL 2025-07 conditional novelty 6.0 of 10

    An end-to-end simultaneous speech-to-speech translation model with voice cloning, trained with a two-stage reinforcement learning reward scheme, reports high accuracy and low latency on the authors' RealSI benchmark.

  4. VerIF: Verification Engineering for Reinforcement Learning in Instruction Following

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A hybrid verifier that combines rule-based code checks and a reasoning-LLM judge enables reinforcement learning to improve LLM instruction following on several benchmarks.

  5. TAT-R1: Terminology-Aware Translation with Reinforcement Learning and Word Alignment

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Word-alignment rewards for RL-trained translation raise terminology accuracy on RTT from 54.42 to 56.42 TA without hurting general translation quality.

  6. Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning

    cs.CL 2026-07 conditional novelty 5.0 of 10

    On Swiss legal translation, reinforcement learning with a ChrF reward improves small open models more than supervised fine-tuning, but frontier reasoning models still score higher.

  7. RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation

    cs.CL 2025-06 conditional novelty 5.0 of 10

    RIVAL iteratively re-trains a reward model adversarially against the current translator and adds a BLEU-predicting head, improving in-domain WMT and subtitle translation over SFT baselines.

  8. How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Large reasoning models such as OpenAI-o1, DeepSeek-R1, and Gemini-2.0-Flash-Thinking score higher than traditional LLMs on semantic quality metrics in complex and document-level translation, but lag on BLEU and in ter...

  9. TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration

    cs.CL 2025-06 conditional novelty 4.0 of 10

    TACTIC, a cognitive-inspired six-agent workflow, improves LLM translation quality over direct prompting on FLORES-200 and WMT24, with the best DeepSeek-V3 setup reaching 96.19 XCOMET on English-to-X.

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