Marco Large Translation Model at WMT 2025: Transforming Translation Capability in LLM s via Quality-Aware Training and Decoding (2025)

Abstract: This paper presents the Marco-MT-Algharb system, our submission to the WMT2025 General Machine Translation Shared Task from Alibaba International Digital Commerce (AIDC). Built on a large language model (LLM) foundation, the system’s strong performance stems from novel quality-aware training and decoding techniques: (1) a two-step supervised fine-tuning (SFT) process incorporating data distillation, (2) a two-step reinforcement learning (RL) framework for preference alignment, and (3) a hybrid decoding strategy that integrates word alignment with Minimum Bayes Risk (MBR) re-ranking to improve faithfulness. These approaches jointly ensure high accuracy and robustness across diverse languages and domains. In the official human evaluation, our system secured five first‐place finishes, one second, and four third‐place results in the constrained category across the 13 directions we participated in. Notably, for the English-Chinese, our results surpassed all open/closed‐source systems.

## introduction the conference on machine translation (wmt) continues to be the primary arena for benchmarking the advancements in machine translation technology @xcite . for years, the field was dominated by the transformer architecture @xcite , which set a high standard for translation quality through its powerful attention mechanism. however, the recent advent of large language models (llms) has sparked a paradigm shift. these models, pre-trained on vast amounts of text data, have demonstrated remarkable capabilities in understanding context, generating fluent text, and leveraging world knowledge, making them exceptionally promising candidates for complex multilingual translation tasks @xcite @xcite lang. pair human evaluation alves et al., @xcite . however, adapting these powerful, general-purpose llms for high-fidelity, specialized translation presents a significant challenge @xcite . to bridge this gap, we propose quality-aware training and decoding techniques designed to systematically enhance both the fluency and faithfulness of llm-based translation. to this end, we present the marco-mt-algharb system. marco-mt-algharb is our submission to the wmt 2025 general machine translation shared task. our participation covers 13 diverse language pairs. 1 built upon the qwen3-14b foundation 1 the 13 language pairs are: english to chinese (@xmath0, english to japanese (@xmath1, english to korean (@xmath2, english to egyptian arabic (@xmath3, english to bhojpuri (@xmath4, english to czech (@xmath5, english to estonian (@xmath6, english to russian (@xmath7, english to ukrainian (@xmath8, english to serbian (latin script) (@xmath9, czech to german (@xmath10, czech to ukrainian (@xmath11, and japanese to chinese (@xmath12. model @xcite , we propose qualityaware training and decoding techniques to enhance translation quality through three key phases: (1) a two-step supervised fine-tuning (sft) process with data distillation to expand data coverage; our work makes several key contributions to the development of state-of-the-art, llm-based translation systems: the remainder of this paper is structured as follows: section 2 provides a detailed overview of our system's architecture and training methodology. section 3 presents our experimental setup and results. finally, section 4 concludes the paper. ## conclusion this paper presented marco-mt-algharb, our system for the wmt25 general machine translation shared task. we introduced a novel qualityaware framework that enhances llm-based translation through a synergistic combination of twostep supervised fine-tuning with data distillation, dynamic multi-reward reinforcement learning, and a hybrid alignment-aware decoding strategy. our methodology was validated on the wmt25 test set, where marco-mt-algharb substantially outperformed strong baselines and leading proprietary models. these results were corroborated by the official human evaluation, which placed our system in the top three across 12 of our 13 language pairs, including six first-place victories. notably, in the highly competitive english-chinese direction, our system ranked first among all open and closed-source submissions.