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

arXiv: https://arxiv.org/abs/2502.19735 · HTML: https://mlsanity.com/paper/2502.19735/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 40 of its 82 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 30 of 40)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training](https://mlsanity.com/paper/2501.17161/) (2025)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://mlsanity.com/paper/2310.10482/) (2023)
- [Better Zero-Shot Reasoning with Role-Play Prompting](https://mlsanity.com/paper/2308.07702/) (2023)
- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions](https://mlsanity.com/paper/2411.14405/) (2024)
- [O1 Replication Journey: A Strategic Progress Report -- Part 1](https://mlsanity.com/paper/2410.18982/) (2024)
- [Towards Making the Most of ChatGPT for Machine Translation](https://mlsanity.com/paper/2303.13780/) (2023)
- [Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal](https://mlsanity.com/paper/2403.01244/) (2024)
- [Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities](https://mlsanity.com/paper/2404.17790/) (2024)
- [Revisiting Catastrophic Forgetting in Large Language Model Tuning](https://mlsanity.com/paper/2406.04836/) (2024)
- [A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models](https://mlsanity.com/paper/2309.11674/) (2023)
- [On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting](https://mlsanity.com/paper/2206.00761/) (2022)
- [o1-Coder: an o1 Replication for Coding](https://mlsanity.com/paper/2412.00154/) (2024)
- [Do Multilingual LLMs Think In English?](https://mlsanity.com/paper/2502.15603/) (2025)
- [Adapting Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2401.06468/) (2024)
- [Benchmarking Machine Translation with Cultural Awareness](https://mlsanity.com/paper/2305.14328/) (2023)
- [Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap](https://mlsanity.com/paper/2501.02448/) (2025)
- [PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning](https://mlsanity.com/paper/2311.08711/) (2023)
- [TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement](https://mlsanity.com/paper/2402.16379/) (2024)

## Built on by (most cited first, 21 of 21)

- [MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning](https://mlsanity.com/paper/2504.10160/) (2025)
- [VerIF: Verification Engineering for Reinforcement Learning in Instruction Following](https://mlsanity.com/paper/2506.09942/) (2025)
- [SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation](https://mlsanity.com/paper/2505.16637/) (2025)
- [An Efficient and Precise Training Data Construction Framework for Process-supervised Reward Model in Mathematical Reasoning](https://mlsanity.com/paper/2503.02382/) (2025)
- [ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement Learning](https://mlsanity.com/paper/2505.12996/) (2025)
- [Seed LiveInterpret 2.0: End-to-end Simultaneous Speech-to-speech Translation with Your Voice](https://mlsanity.com/paper/2507.17527/) (2025)
- [DeepTrans: Deep Reasoning Translation via Reinforcement Learning](https://mlsanity.com/paper/2504.10187/) (2025)
- [How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation](https://mlsanity.com/paper/2505.19987/) (2025)
- [TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration](https://mlsanity.com/paper/2506.08403/) (2025)
- [Test-Time Scaling of Reasoning Models for Machine Translation](https://mlsanity.com/paper/2510.06471/) (2025)
- [Scaling Large Reasoning Models beyond Human Supervision: A Path toward Superintelligence](https://mlsanity.com/paper/2608.31075/) (2026)
- [RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation](https://mlsanity.com/paper/2506.05070/) (2025)
- [LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens](https://mlsanity.com/paper/2510.11919/) (2025)
- [R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning](https://mlsanity.com/paper/2509.25987/) (2025)
- [Chart Specification: Structural Representations for Incentivizing VLM Reasoning in Chart-to-Code Generation](https://mlsanity.com/paper/2602.10880/) (2026)
- [Chain-of-Thought Reasoning Improves Context-Aware Translation with Large Language Models](https://mlsanity.com/paper/2510.18077/) (2025)
- [The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models](https://mlsanity.com/paper/2604.20225/) (2026)
- [Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?](https://mlsanity.com/paper/2606.03782/) (2026)
- [Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning](https://mlsanity.com/paper/2607.19181/) (2026)
- [Reasoning about In-Context Samples for Machine-Translation](https://mlsanity.com/paper/2608.27036/) (2026)
- [When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of LRM Reasoning Traces](https://mlsanity.com/paper/2609.21247/) (2026)

## Abstract (arXiv)

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.