Shy-hunyuan- MT at WMT 25 General Machine Translation Shared Task (2025)

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 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.

## introduction the field of machine translation has witnessed remarkable progress with the emergence of large language models; yet, challenges remain in consistently producing human-quality translations. this work addresses two key limitations: (1) the overreliance on single-model supervision during finetuning, and (2) the difficulty of aligning machine outputs with nuanced human judgments during reinforcement learning. our method comprises three main phases. first, we collect diverse translations from state-of-theart open-source large language models, including @xcite , @xcite , and gemma across multiple language pairs. this ensemble approach provides a richer training signal than single-model distillation, exposing our base model hunyuan-7b to varied translation strategies and stylistic choices. the collected translation outputs are carefully filtered and normalized before serving as supervision targets. second, we perform supervised fine-tuning (sft) on hunyuan-7b using the prior collected translation dataset. crucially, we implement a dynamic weighting scheme that prioritizes higher-quality translations during sft training, as determined by automatic metrics. specifically, this phase enables the model to internalize the strengths of each contributor model while maintaining its own linguistic identity. the third phase applies group relative policy optimization @xcite , a sampleefficient reinforcement learning (rl) algorithm, to further refine the model. we employ xcomet for its strong correlation with human judgments and deepseek-v3 for its complementary strengths in fluency assessment. the reward function combines these signals with a kl-divergence term to prevent excessive deviation from the sft model. during rl training, we maintain multiple policy groups that explore different translation strategies, with periodic selection pressure favoring the approaches that yield the highest rewards. our methodology incorporates several primary techniques to ensure robust performance. specifically, we employ a temperature-scaled sampling strategy during preference data collection to optimize the trade-off between diversity and quality. for the sft phase, a layer-wise learning rate decay is applied to enhance model adaptation. during the rl phase, we implement a dynamic reward normalization scheme to maintain training stability. ## related work this section reviews the key research strands underlying our approach: (1) large language models for machine translation, (2) continual pre-training strategies, (3) supervised fine-tuning for translation tasks, and (4) reinforcement learning optimization for text generation. ## methodology our approach consists of two major phases: (1) a three-stage training pipeline for developing a highquality base translation model, and (2) an ensemble strategy that leverages diversity generation and reinforcement learning to produce final translations. the overall architecture is illustrated in figure 1 . ## conclusion in this work, we propose shy-hunyuan-mt, a novel mt system built upon the synergy-enhanced policy optimization framework (shy). our method leverages a carefully designed two-phase training paradigm that systematically transforms the open-sourced hunyuan-7b base model into a stateof-the-art translation system. the core innovation of our method lies in the synergistic combination of three complementary training phases: domain-adaptive continual pre-training on largescale parallel corpora, supervised fine-tuning on curated wmt datasets, and reinforcement learning through generalized reward policy optimization (grpo) with composite reward signals. this progressive training strategy enables the model to acquire robust multilingual translation capabilities while maintaining strong performance across a diverse range of language pairs. our extensive experiments on 31 language directions from wmt25 demonstrate the effectiveness of shy-hunyuan-mt. the model achieves consistent top-tier performance with an autorank score of 1.0 across all evaluated directions, while excelling on multiple automatic metrics, including xcomet-xl, cometkiwi-xl, and gemba-esa variants. notably, the model demonstrates its ability to handle both high-resource and extremely low-resource language pairs with comparable proficiency, highlighting its strong generalization capabilities and effectiveness in cross-lingual transfer learning. the success of our approach validates several key design decisions: (1) the importance of domain-specific continual pre-training in adapting general-purpose llms for translation tasks, (2) the effectiveness of incorporating multiple cometbased metrics as reward signals during policy optimization, and (3) the value of progressive training paradigms in building robust multilingual systems. these findings provide valuable insights for future research in neural machine translation and crosslingual model adaptation. looking forward, shyhunyuan-mt establishes a strong foundation for advancing multilingual translation technology, particularly in scenarios involving diverse language families and resource constraints. the framework's flexibility and consistent performance across varied linguistic contexts position it as a promising solution for real-world translation applications and a solid baseline for future improvements in the field.