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    <abstract>We present Qingqiu-MT-9B, an instruction-following multilingual translation model developed for the constrained track of the WMT26 General Machine Translation shared task. Our model supports translation among 20 languages and covers 22 of the 23 official language pairs. It uses a source-grounded synthesis pipeline with separate LLM-based steps for generating instance-specific translation instructions and their corresponding translations. The resulting corpus covers diverse translation requirements, task complexity, instruction formulations, domains, document lengths, and glossary constraints, reducing reliance on fixed prompt templates and supporting generalization to unseen translation instructions. Our model is based on Qwen3.5-9B and trained through two-stage full-parameter supervised fine-tuning (SFT) followed by reinforcement learning (RL). The first SFT stage performs broad adaptation on a large-scale, medium-quality corpus, while the second refines the model on a smaller, higher-quality set. The RL stage further optimizes the model with Group Relative Policy Optimization (GRPO). During development, we use an LLM-based, rubric-guided contrastive method that scores candidates by dimension and aggregates an overall score to assess translation quality. Experiments show that Qingqiu-MT-9B demonstrates strong generalization and competitiveness among similarly sized models.We release the model at https://huggingface.co/WPS-Qingqiu/Qingqiu-MT-9B.</abstract>
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%0 Conference Proceedings
%T QINGQIU-MT-9B: An Instruction-Following Multilingual Translation Model
%A Xia, Tian
%A Chen, Chao
%A Yang, Mengpeng
%A Yang, Jingxu
%A Sun, Yabo
%A Liu, Qiang
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F xia-etal-2026-qingqiu
%X We present Qingqiu-MT-9B, an instruction-following multilingual translation model developed for the constrained track of the WMT26 General Machine Translation shared task. Our model supports translation among 20 languages and covers 22 of the 23 official language pairs. It uses a source-grounded synthesis pipeline with separate LLM-based steps for generating instance-specific translation instructions and their corresponding translations. The resulting corpus covers diverse translation requirements, task complexity, instruction formulations, domains, document lengths, and glossary constraints, reducing reliance on fixed prompt templates and supporting generalization to unseen translation instructions. Our model is based on Qwen3.5-9B and trained through two-stage full-parameter supervised fine-tuning (SFT) followed by reinforcement learning (RL). The first SFT stage performs broad adaptation on a large-scale, medium-quality corpus, while the second refines the model on a smaller, higher-quality set. The RL stage further optimizes the model with Group Relative Policy Optimization (GRPO). During development, we use an LLM-based, rubric-guided contrastive method that scores candidates by dimension and aggregates an overall score to assess translation quality. Experiments show that Qingqiu-MT-9B demonstrates strong generalization and competitiveness among similarly sized models.We release the model at https://huggingface.co/WPS-Qingqiu/Qingqiu-MT-9B.
%U https://aclanthology.org/2026.wmt-main.83/
%P 1447-1462

Markdown (Informal)

[QINGQIU-MT-9B: An Instruction-Following Multilingual Translation Model](https://aclanthology.org/2026.wmt-main.83/) (Xia et al., WMT 2026)

• QINGQIU-MT-9B: An Instruction-Following Multilingual Translation Model (Xia et al., WMT 2026)

ACL

• Tian Xia, Chao Chen, Mengpeng Yang, Jingxu Yang, Yabo Sun, and Qiang Liu. 2026. QINGQIU-MT-9B: An Instruction-Following Multilingual Translation Model. In Proceedings of the Eleventh Conference on Machine Translation, pages 1447–1462, Budapest, Hungary. Association for Computational Linguistics.