Y andex Submission to the WMT 25 General Machine Translation Task (2025)

Abstract: This paper describes Yandex submission to the WMT25 General Machine Translation task. We participate in English-to-Russian translation direction and propose a purely LLM-based translation model. Our training procedure comprises a training pipeline of several stages built upon YandexGPT, an in-house general-purpose LLM. In particular, firstly, we employ continual pretraining (post-pretrain) for MT task for initial adaptation to multilinguality and translation. Subsequently, we use SFT on parallel document-level corpus in the form of P-Tuning. Following SFT, we propose a novel alignment scheme of two stages, the first one being a curriculum learning with difficulty schedule and a second one - training the model for tag preservation and error correction with human post-edits as training samples. Our model achieves results comparable to human reference translations on multiple domains.

## introduction we participate in the wmt25 general machine translation task and propose a purely llm-based translation system. large language models (llms) have recently redefined the state-of-the-art in machine translation, demonstrating strong capabilities that yield near-human quality outputs on vast collection of language pairs. their performance has consistently surpassed that of the previous generation of specialized neural machine translation (nmt) systems, marking a significant paradigm shift in the field. the recent wmt24 general machine translation contest provides compelling evidence of this trend, where top-performing systems, predominantly based on llms, achieved translation scores remarkably close to human reference translations. still, the human parity claim remains disputable and thorough evaluations have shown that for a variety of high-resource morphologically rich languages, although quite high, the performance of llms still lags behind professional human translations. all state-of-the-art llm systems exhibit a noticeable pattern of literal translations with fluency and naturalness of generations significantly worse than that of native human translations. in this work, we propose a novel pipeline for llm adaptation to the mt task, building upon our previous year submission @xcite . the main goal of this work is to explore tools and techniques for improving the performance of an already capable mt-specific llm model with near human performance. the system comprises a pipeline of several adaptation stages built upon 7-billion yandexgpt, an inhouse proprietary general-purpose language model. the resulting model (yandex) was subsequently fine-tuned on the wmt dataset, producing the yan-dex+wmt model which constitutes our submis-sion to the wmt25 general machine translation task. for the english-to-russian translation direction our resulting system is significantly better than all of the previous year contenders and achieves results comparable to major foundational llms on this year's benchmark. ## system overview in this section we describe the training pipeline of the system and details of the inference procedure. we also provide automatic metrics and human evaluation results for the key components of the system. ## human evaluation results due to constraints on time and human resources we selected subsets from two testsets for human evaluation: 495 segments from wmt24 general mt testset and 136 segments from wmt25 general mt blindset. these subsets are hereafter referred to as wmt24 and wmt25, respectively. obviously, the human translations of wmt25 testset could not be included in this evaluation campaign since they have not been publicly released yet. the number of segments and their distribution across domains is presented in two protocols were implemented to evaluate mt quality of our submission: the esa protocol @xcite , following wmt guidelines, and the rate protocol, introduced in @xcite . the annotators' qualifications and detailed annotation setup are described in appendix a. yandex+wmt is compared to yandex model (referred to as dpo stage 2 + tags above) and several llm translations. claude3.7 and gpt-4 translations for wmt24 testset were obtained directly from wmt24 publicly released data. the wmt25 testset was translated using claude 4 and gpt-4.1 with a simple prompt "you are a professional english-to-russian translator. your goal is to accurately convey the meaning and nuances of the original english text while adhering to russian grammar, vocabulary, and cultural sensitivities. produce only the russian translation, without any additional explanations or commentary. translate the following text: {input text}". we report segment-level esa scores alongside error counts and mqm-like scores calculated as 5×major+minor in ## conclusion in this paper, we describe yandex submission to the wmt25 general translation task. for englishto-russian translation direction, our model outperforms all systems from wmt24 competition and achieves results comparable to major foundational llms on wmt25 benchmark, as measured by esa and rate human evaluation protocols. according to the official human evaluation results our model achieves parity with human reference translations on esa score. we present a detailed description of our training procedure as well as giving the rationale for different training steps. our pipeline includes multistage alignment procedure specifically designed to improve the quality of an already capable machine translation system, with the performance close to the human one. we employ novel techniques, such as curriculum learning with sentences-to-documents adaptation, training on human post-edits and fine-tuning for tagged data using rule-based reward. our results, measured both in automatic metrics and human evaluations, demonstrate the effectiveness of the proposed pipeline as well as high overall translation quality of the system.