@inproceedings{wang-etal-2026-lumen,
title = "Lumen at {WMT}2026: Instruction-Specialized Translation via Quality-Aware Training and On-Policy Distillation",
author = "Wang, Hao and
Liu, Yangyang and
Zhao, Xiaohu and
Liu, Heng and
Shang, Zifu and
Li, Tianhao and
Gao, Ruize and
Tang, Jialong and
Shao, Shiao and
Wei, Haoran and
Yang, Baosong and
Xu, Linlong and
Wang, Longyue and
Luo, Weihua",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.81/",
pages = "1430--1439",
ISBN = "979-8-89176-474-3",
abstract = "We present Lumen, our submission to the WMT2026 General Machine Translation Shared Task. We focus on instruction-specialized translation, where systems must produce accurate translations while satisfying fine-grained requirements on style, terminology, and structured output. Lumen follows a four-stage pipeline. First, continued pre-training and supervised fine-tuning jointly strengthen multilingual translation competence and the ability to follow translation-specific instructions, such as terminology, style, and format constraints. Second, we extend the originally offline M{\{}2{\}}PO preference-optimization framework into an online preference-optimization stage: the current policy samples candidate translations, an LLM judge assigns continuous direct-assessment scores, and the resulting multi-pair preferences are used to update the model. Third, on-policy distillation transfers stronger translation-instruction-following behavior from a larger teacher model to the Qwen3-14B student, improving translation-instruction adherence while retaining a 14B primary translation backbone. Finally, structure-aware inference and postprocessing preserve required formats, while selective repair addresses clear structural or decoding failures before submission. We participate in 23 translation directions. Under an absolute 1{--}5 LLM-judge rubric that evaluates translation-instruction following and translation quality separately, Lumen reaches an overall mean score of 4.60. Despite using a 14B translation backbone, it achieves performance comparable to leading proprietary systems in both translation quality and translation-instruction following."
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<abstract>We present Lumen, our submission to the WMT2026 General Machine Translation Shared Task. We focus on instruction-specialized translation, where systems must produce accurate translations while satisfying fine-grained requirements on style, terminology, and structured output. Lumen follows a four-stage pipeline. First, continued pre-training and supervised fine-tuning jointly strengthen multilingual translation competence and the ability to follow translation-specific instructions, such as terminology, style, and format constraints. Second, we extend the originally offline M{2}PO preference-optimization framework into an online preference-optimization stage: the current policy samples candidate translations, an LLM judge assigns continuous direct-assessment scores, and the resulting multi-pair preferences are used to update the model. Third, on-policy distillation transfers stronger translation-instruction-following behavior from a larger teacher model to the Qwen3-14B student, improving translation-instruction adherence while retaining a 14B primary translation backbone. Finally, structure-aware inference and postprocessing preserve required formats, while selective repair addresses clear structural or decoding failures before submission. We participate in 23 translation directions. Under an absolute 1–5 LLM-judge rubric that evaluates translation-instruction following and translation quality separately, Lumen reaches an overall mean score of 4.60. Despite using a 14B translation backbone, it achieves performance comparable to leading proprietary systems in both translation quality and translation-instruction following.</abstract>
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%0 Conference Proceedings
%T Lumen at WMT2026: Instruction-Specialized Translation via Quality-Aware Training and On-Policy Distillation
%A Wang, Hao
%A Liu, Yangyang
%A Zhao, Xiaohu
%A Liu, Heng
%A Shang, Zifu
%A Li, Tianhao
%A Gao, Ruize
%A Tang, Jialong
%A Shao, Shiao
%A Wei, Haoran
%A Yang, Baosong
%A Xu, Linlong
%A Wang, Longyue
%A Luo, Weihua
%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 wang-etal-2026-lumen
%X We present Lumen, our submission to the WMT2026 General Machine Translation Shared Task. We focus on instruction-specialized translation, where systems must produce accurate translations while satisfying fine-grained requirements on style, terminology, and structured output. Lumen follows a four-stage pipeline. First, continued pre-training and supervised fine-tuning jointly strengthen multilingual translation competence and the ability to follow translation-specific instructions, such as terminology, style, and format constraints. Second, we extend the originally offline M{2}PO preference-optimization framework into an online preference-optimization stage: the current policy samples candidate translations, an LLM judge assigns continuous direct-assessment scores, and the resulting multi-pair preferences are used to update the model. Third, on-policy distillation transfers stronger translation-instruction-following behavior from a larger teacher model to the Qwen3-14B student, improving translation-instruction adherence while retaining a 14B primary translation backbone. Finally, structure-aware inference and postprocessing preserve required formats, while selective repair addresses clear structural or decoding failures before submission. We participate in 23 translation directions. Under an absolute 1–5 LLM-judge rubric that evaluates translation-instruction following and translation quality separately, Lumen reaches an overall mean score of 4.60. Despite using a 14B translation backbone, it achieves performance comparable to leading proprietary systems in both translation quality and translation-instruction following.
%U https://aclanthology.org/2026.wmt-main.81/
%P 1430-1439Markdown (Informal)
[Lumen at WMT2026: Instruction-Specialized Translation via Quality-Aware Training and On-Policy Distillation](https://aclanthology.org/2026.wmt-main.81/) (Wang et al., WMT 2026)
ACL
- Hao Wang, Yangyang Liu, Xiaohu Zhao, Heng Liu, Zifu Shang, Tianhao Li, Ruize Gao, Jialong Tang, Shiao Shao, Haoran Wei, Baosong Yang, Linlong Xu, Longyue Wang, and Weihua Luo. 2026. Lumen at WMT2026: Instruction-Specialized Translation via Quality-Aware Training and On-Policy Distillation. In Proceedings of the Eleventh Conference on Machine Translation, pages 1430–1439, Budapest, Hungary. Association for Computational Linguistics.