ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced
  Reinforcement Learning
In recent years, the emergence of large reasoning models (LRMs), such as
OpenAI-o1 and DeepSeek-R1, has shown impressive capabilities in complex
problems, e.g., mathematics and coding. Some pioneering studies attempt to
bring the success of LRMs in neural machine translation (MT). They try to build
LRMs with deep reasoning MT ability via reinforcement learning (RL). Despite
some progress that has been made, these attempts generally focus on several
high-resource languages, e.g., English and Chinese, leaving the performance on
other languages unclear. Besides, the reward modeling methods in previous work
do not fully unleash the potential of reinforcement learning in MT. In this
work, we first design a new reward modeling method that compares the
translation results of the policy MT model with a strong LRM (i.e.,
DeepSeek-R1-671B), and quantifies the comparisons to provide rewards.
Experimental results demonstrate the superiority of the reward modeling method.
Using Qwen2.5-7B-Instruct as the backbone, the trained model achieves the new
state-of-the-art performance in literary translation, and outperforms strong
LRMs including OpenAI-o1 and DeepSeeK-R1. Furthermore, we extend our method to
the multilingual settings with 11 languages. With a carefully designed
lightweight reward modeling in RL, we can simply transfer the strong MT ability
from a single direction into multiple (i.e., 90) translation directions and
achieve impressive multilingual MT performance.
682c1f8b47e6c8a0c0fd5b5f
Jiaan Wang
admin_assigned
2025-05-20T08:46:01.647Z
6051e3f145db307eddc0c962
https://cdn-avatars.huggingface.co/v1/production/uploads/1676443438507-6051e3f145db307eddc0c962.jpeg
Jiaan Wang
user
Krystalan
682c1f8b47e6c8a0c0fd5b60
Fandong Meng
admin_assigned
2025-05-20T08:51:05.844Z
64cb254871a7bbb60c17d5fa
/avatars/5121fd5b7b55d275eba3947f3f4c034d.svg
Fandong Meng
user
fandong
682c1f8b47e6c8a0c0fd5b61
Jie Zhou
ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced
  Reinforcement Learning
In recent years, the emergence of large reasoning models (LRMs), such as
OpenAI-o1 and DeepSeek-R1, has shown impressive capabilities in complex
problems, e.g., mathematics and coding. Some pioneering studies attempt to
bring the success of LRMs in neural machine translation (MT). They try to build
LRMs with deep reasoning MT ability via reinforcement learning (RL). Despite
some progress that has been made, these attempts generally focus on several
high-resource languages, e.g., English and Chinese, leaving the performance on
other languages unclear. Besides, the reward modeling methods in previous work
do not fully unleash the potential of reinforcement learning in MT. In this
work, we first design a new reward modeling method that compares the
translation results of the policy MT model with a strong LRM (i.e.,
DeepSeek-R1-671B), and quantifies the comparisons to provide rewards.
Experimental results demonstrate the superiority of the reward modeling method.
Using Qwen2.5-7B-Instruct as the backbone, the trained model achieves the new
state-of-the-art performance in literary translation, and outperforms strong
LRMs including OpenAI-o1 and DeepSeeK-R1. Furthermore, we extend our method to
the multilingual settings with 11 languages. With a carefully designed
lightweight reward modeling in RL, we can simply transfer the strong MT ability
from a single direction into multiple (i.e., 90) translation directions and
achieve impressive multilingual MT performance.
A new reward modeling method enhances multilingual neural machine translation by leveraging a strong reasoning model and achieves state-of-the-art performance.
large reasoning models reinforcement learning neural machine translation reward modeling literary translation multilingual translation