TranslateGemma Technical Report
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.
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Mara Finkelstein
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Isaac Caswell
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Tobias Domhan
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Jan-Thorsten Peter
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Juraj Juraska
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Parker Riley
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Daniel Deutsch
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Cole Dilanni
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Colin Cherry
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Eleftheria Briakou
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Elizabeth Nielsen
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Jiaming Luo
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Kat Black
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Ryan Mullins
claimed_verified
2026-01-15T15:02:50.659Z
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Ryan Mullins
RyanMullins
user
RyanMullins
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Sweta Agrawal
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Wenda Xu
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Erin Kats
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Stephane Jaskiewicz
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Markus Freitag
696869da0ac10a06522f6aab
David Vilar
TranslateGemma Technical Report
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.
TranslateGemma enhances Gemma 3's multilingual capabilities through two-stage fine-tuning with synthetic and human-translated data, achieving superior translation quality with improved efficiency.
machine translation Gemma 3 two-stage fine-tuning supervised fine-tuning reinforcement learning reward models MetricX-QE AutoMQM WMT25 WMT24++ Vistra