DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
Authors: name: Jiaan Wang, name: Fandong Meng, name: Yunlong Liang, name: Jie Zhou

Recently, O1-like models have emerged as representative examples,
illustrating the effectiveness of long chain-of-thought (CoT) in reasoning
tasks such as math and coding tasks. In this paper, we introduce DRT-o1, an
attempt to bring the success of long CoT to neural machine translation (MT).
Specifically, in view of the literature books that might involve similes and
metaphors, translating these texts to a target language is very difficult in
practice due to cultural differences. In such cases, literal translation often
fails to convey the intended meaning effectively. Even for professional human
translators, considerable thought must be given to preserving semantics
throughout the translation process. To simulate LLMs' long thought ability in
MT, we first mine sentences containing similes or metaphors from existing
literature books, and then develop a multi-agent framework to translate these
sentences via long thought. In the multi-agent framework, a translator is used
to iteratively translate the source sentence under the suggestions provided by
an advisor. To ensure the effectiveness of the long thoughts, an evaluator is
also employed to judge whether the translation in the current round is better
than the previous one or not. In this manner, we collect tens of thousands of
long-thought MT data, which is used to train our DRT-o1. The experimental
results on literature translation demonstrate the effectiveness of the DRT-o1.
Using Qwen2.5-7B and Qwen2.5-14B as the backbones, the improvement brought by
DRT-o1 achieves 7.33~8.26 BLEU and 1.66~3.36 CometScore. Besides, DRT-o1-7B can
outperform QwQ-32B-Preview by 7.82 BLEU and 1.46 CometScore, showing its
effectiveness. The project is available at https://github.com/krystalan/DRT-o1