Gemma技术报告

摘要

我们推出TranslateGemma——一套基于Gemma 3基础模型的开源机器翻译模型。为增强Gemma 3原生多语言能力在翻译任务中的表现，我们采用两阶段微调策略：首先利用通过前沿模型生成的大规模高质量合成平行数据与人工翻译平行数据组成的混合数据集进行监督微调；随后通过强化学习阶段，采用包含MetricX-QE和AutoMQM在内的奖励模型组合优化翻译质量。我们在WMT25测试集的10个语言对上开展人工评估，并在WMT24++基准测试的55个语言对上进行自动评估，结果验证了TranslateGemma的有效性。自动指标显示所有规模的模型均较基线Gemma 3模型取得持续显著提升。值得注意的是，较小体量的TranslateGemma模型常能达到与更大基线模型相媲美的性能，同时具有更优的效率。我们还证明TranslateGemma模型保留了强大的多模态能力，在Vistra图像翻译基准测试中表现出增强性能。本次开源TranslateGemma模型旨在为研究社区提供强大且适应性强的机器翻译工具。

English

We present TranslateGemma, a suite of open machine translation models based on the Gemma 3 foundation models. To enhance the inherent multilingual capabilities of Gemma 3 for the translation task, we employ a two-stage fine-tuning process. First, supervised fine-tuning is performed using a rich mixture of high-quality large-scale synthetic parallel data generated via state-of-the-art models and human-translated parallel data. This is followed by a reinforcement learning phase, where we optimize translation quality using an ensemble of reward models, including MetricX-QE and AutoMQM, targeting translation quality. We demonstrate the effectiveness of TranslateGemma with human evaluation on the WMT25 test set across 10 language pairs and with automatic evaluation on the WMT24++ benchmark across 55 language pairs. Automatic metrics show consistent and substantial gains over the baseline Gemma 3 models across all sizes. Notably, smaller TranslateGemma models often achieve performance comparable to larger baseline models, offering improved efficiency. We also show that TranslateGemma models retain strong multimodal capabilities, with enhanced performance on the Vistra image translation benchmark. The release of the open TranslateGemma models aims to provide the research community with powerful and adaptable tools for machine translation.

Gemma技术报告

TranslateGemma Technical Report

摘要

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