Command-A-Translate: Raising the Bar of Machine Translation with Difficulty Filtering (2025)

Abstract: We present Command A Translate, an LLMbased machine translation model built off Cohere’s Command A. It reaches state-of-the-art machine translation quality via direct preference optimization. Our meticulously designed data preparation pipeline emphasizes robust quality control and a novel difficulty filtering – a key innovation that distinguishes Command A Translate. Furthermore, we extend our model and participate at WMT with a system (CommandA-WMT) that uses two models and post-editing steps of step-by-step reasoning and limited Minimum Bayes Risk decoding.

## introduction neural machine translation (nmt) has revolutionized the field of machine translation @xcite . this paradigm shift has been recently further accelerated by the advent of large language models (llms), which not only excel at following instructions but also demonstrate remarkable capabilities in multilingual multi-domain translation tasks as yearly evaluated at wmt conference @xcite ). yet, despite these gains, translation remains an open challenge. real-world use cases often demand more than producing correct content: systems must adapt to stylistic variation, navigate complex sentence structures, and follow detailed instructions faithfully. these aspects expose weaknesses even in the most advanced models. addressing them is crucial for moving towards translation systems that are not only capable, but also reliable and controllable across diverse contexts. in this paper, we introduce command a translate, a state-of-the-art machine translation system built upon cohere's flagship model, command a @xcite . it achieves unparalleled translation quality through direct preference optimization (dpo), leveraging the robust multilingual performance of its underlying architecture. the key innovation lies in our data preparation pipeline, which incorporates a novel difficulty filtering mechanism to ensure high-quality training data. this approach not only enhances the performance but also sets a new benchmark in the field. we further extend our model to participate in wmt 2025 @xcite , submitting commanda-wmt, which employs a two-model architecture and incorporates post-editing steps such as step-by-step reasoning and limited minimum bayes risk decoding. our results highlight the effectiveness of this design, demonstrating not only consistent gains in translation quality but also the broader potential of llm-based approaches to push the frontier of machine translation. these advances pave the way for translation systems that are not only accurate but also adaptable, controllable, and aligned with diverse human language use. ## training details in this section, we describe the architecture; how the training data is prepared; and how we finetuned off command a for building command a translate and commanda-wmt. ## evaluation and results we analyze the performance of our model and compare it to top-performing open and closed systems. we evaluate all systems including ours in an identical setup unless specified otherwise, in a clean zero-shot approach without any post editing steps. we fix the temperature to 0. the only exception is the commanda-wmt, where we report results as submitted to wmt general mt shared tasks using additional post-editing steps described in section 2.7. ## conclusion we introduce command a translate with deep translation ⊕r capabilities, cohere's stateof-the-art machine translation system. command a translate is built off cohere's command a by fine-tuning on meticulously-prepared datasets and with direct preference optimization. as the key innovation, our data pipeline, incorporates a series of novel data filters, targeting selection of most difficult data subset and strong capabilities across languages. command a translate achieves marked improvement in translation quality, and outperforms other translation systems such as google translate, and state-of-the-art llms such as gpt-5 and gemini-2.5 pro. extending command a translate, we present commanda-wmt, our translation system submission to the 2025 wmt shared task. this system leverages a two-model architecture and post-editing steps such as step-by-step reasoning and limited minimum bayes risk decoding. commanda-wmt achieves consistent gains in across languages, showcasing the effectiveness of our design.