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arXiv · 2510.09189

LLaMAX2: Your Translation-Enhanced Model also Performs Well in Reasoning

Changjiang Gao·Zixian Huang·Jingyang Gong·Shujian Huang·Lei Li·Fei Yuan
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Abstract

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.

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Changjiang Gao, Zixian Huang, Jingyang Gong, Shujian Huang, Lei Li, Fei Yuan. 2025-10-10. LLaMAX2: Your Translation-Enhanced Model also Performs Well in Reasoning. https://arxiv.org/abs/2510.09189

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