LLaMAX2: Your Translation-Enhanced Model also Performs Well in Reasoning
Authors: name: Changjiang Gao, name: Zixian Huang, name: Jingyang Gong, name: Shujian Huang, name: Lei Li, name: Fei Yuan

General Large Language Models (LLMs) excel in reasoning, but those enhanced
for translation struggle with reasoning tasks. To address this, we propose a
novel translationenhanced recipe that begins with instruct models and applies
layer-selective tuning only on parallel data. Following this pipeline, we
introduce the Qwen3-XPlus models, which demonstrate significant improvements in
translation performance across both high- and lowresource languages, achieving
15+ spBLEU and 40+ xComet in low-resource languages, like Swahili.
Interestingly, training only with small parallel datasets, Qwen3-XPlus achieves
an average improvement of 1+ points on 7 multilingual tasks while maintaining
proficiency comparable to the Qwen3 instruct model in 15 popular reasoning
datasets. This work offers a promising approach to multilingual enhancement,
significantly reducing complexity and enhancing accessibility for a wider range
of languages. The code and model are publicly available.