@inproceedings{zheng-etal-2025-shy,
    title = "Shy-hunyuan-{MT} at {WMT}25 General Machine Translation Shared Task",
    author = "Zheng, Mao  and
      Li, Zheng  and
      Du, Yang  and
      Qu, Bingxin  and
      Song, Mingyang",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.36/",
    pages = "607--613",
    ISBN = "979-8-89176-341-8",
    abstract = "In this paper, we present our submission to the WMT25 shared task on machine translation, for which we propose Synergy-enhanced policy optimization framework, named \textit{Shy}. This novel two-phase training framework synergistically combines knowledge distillation and fusion via reinforcement learning.In the first phase, we introduce a multi-stage training framework that harnesses the complementary strengths of multiple state-of-the-art large language models to generate diverse, high-quality translation candidates. These candidates serve as pseudo-references to guide the supervised fine-tuning of our model, Hunyuan-7B, effectively distilling the collective knowledge of multiple expert systems into a single efficient model.In the second phase, we further refine the distilled model through Group Relative Policy Optimization, a reinforcement learning technique that employs a composite reward function. By calculating reward from multiple perspectives, our model ensures better alignment with human preferences and evaluation metrics.Extensive experiments across multiple language pairs demonstrate that our model Shy-hunyuan-MT yields substantial improvements in translation quality compared to baseline approaches. Notably, our framework achieves competitive performance comparable to that of state-of-the-art systems while maintaining computational efficiency through knowledge distillation and strategic ensemble."
}