This repository contains the resources for our paper Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning
Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the "think-then-answer" paradigm, which enhances both accuracy and interpretability. However, current LRMs exhibit two critical limitations when processing non-English languages: (1) They often struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English. These limitations significantly degrade the user experience for non-English speakers and hinder the global deployment of LRMs. To address these limitations, we propose M-Thinker, which is trained by the GRPO algorithm that involves a Language Consistency (LC) reward and a novel Cross-lingual Thinking Alignment (CTA) reward. Specifically, the LC reward defines a strict constraint on the language consistency between the input, thought, and answer. Besides, the CTA reward compares the model's non-English reasoning paths with its English reasoning path to transfer its own reasoning capability from English to non-English languages. Through an iterative RL procedure, our M-Thinker-1.5B/7B models not only achieve nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath), but also exhibit excellent generalization on out-of-domain languages.
| Model Access | Backbone | Training data Access |
|---|---|---|
| M-Thinker-7B-Iter2 (👍👍) | M-Thinker-7B-Iter1 | M-Thinker-7B-RL-Iter2-data |
| M-Thinker-7B-Iter1 (👍) | 7B-cold-start-SFT | M-Thinker-7B-RL-Iter1-data |
| 7B-cold-start-SFT | DeepSeek-R1-Distill-Qwen-7B | M-Thinker-SFT-data |
| M-Thinker-1.5B-Iter2 (👍👍) | M-Thinker-1.5B-Iter1 | M-Thinker-1.5B-RL-Iter2-data |
| M-Thinker-1.5B-Iter1 (👍) | 1.5B-cold-start-SFT | M-Thinker-1.5B-RL-Iter1-data |
| 1.5B-cold-start-SFT | DeepSeek-R1-Distill-Qwen-1.5B | M-Thinker-SFT-data |
If you find this work useful, please consider citing our paper:
@misc{zhang2025thinknativelyunlockingmultilingual,
title={Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning},
author={Xue Zhang and Yunlong Liang and Fandong Meng and Songming Zhang and Kaiyu Huang and Yufeng Chen and Jinan Xu and Jie Zhou},
year={2025},
eprint={2510.07300},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2510.07300},
}
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1.0
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3