@inproceedings{kocmi-etal-2026-north,
title = "North Small Translate: Advanced Cost-Effective Translation (Cohere {CAT}+)",
author = "Kocmi, Tom and
Berard, Alexandre and
Blunsom, Phil and
Cahyawijaya, Samuel and
Cassini, Shaun and
De Gibert, Ona and
Gomez, Aidan and
Govindarajan, Nithya and
Kiyono, Shun and
Lasche, Olivia and
Rogers, Lawrence and
Marchisio, Kelly and
Moghe, Nikita and
More, Yash and
Moran-Hidalgo, Camila and
Nan, Yiyang and
Sachs, Michael and
Starostina, Trisha and
Van Stigt, Daan and
Rarrick, Spencer and
Vincent, Sebastian and
Zhang, Ivan and
Frosst, Nicholas",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.70/",
pages = "1305--1317",
ISBN = "979-8-89176-474-3",
abstract = "We present North Small Translate, an open-weight, LLM-based machine translation (MT) model with instruction-following capabilities built on the same foundation as Cohere{'}s Command A Plus, a mixture-of-experts architecture with 25 billion active parameters out of 218 billion total parameters. North Small Translate is trained using difficulty sampling to obtain challenging documents and a five-step training protocol combining supervised fine-tuning, direct preference optimization, and online reinforcement learning. We prioritized throughput through a non-reasoning base model and supplemented with optional agentic capabilities to unlock translation quality gains. North Small Translate is trained to perform MT-related tasks, including post-editing and quality estimation, as well as related tasks such as general instruction following. The model achieves top MT performance across 50 languages in the class of models under 1T parameters, with no need to run expensive reasoning at inference time."
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<abstract>We present North Small Translate, an open-weight, LLM-based machine translation (MT) model with instruction-following capabilities built on the same foundation as Cohere’s Command A Plus, a mixture-of-experts architecture with 25 billion active parameters out of 218 billion total parameters. North Small Translate is trained using difficulty sampling to obtain challenging documents and a five-step training protocol combining supervised fine-tuning, direct preference optimization, and online reinforcement learning. We prioritized throughput through a non-reasoning base model and supplemented with optional agentic capabilities to unlock translation quality gains. North Small Translate is trained to perform MT-related tasks, including post-editing and quality estimation, as well as related tasks such as general instruction following. The model achieves top MT performance across 50 languages in the class of models under 1T parameters, with no need to run expensive reasoning at inference time.</abstract>
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%0 Conference Proceedings
%T North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)
%A Kocmi, Tom
%A Berard, Alexandre
%A Blunsom, Phil
%A Cahyawijaya, Samuel
%A Cassini, Shaun
%A De Gibert, Ona
%A Gomez, Aidan
%A Govindarajan, Nithya
%A Kiyono, Shun
%A Lasche, Olivia
%A Rogers, Lawrence
%A Marchisio, Kelly
%A Moghe, Nikita
%A More, Yash
%A Moran-Hidalgo, Camila
%A Nan, Yiyang
%A Sachs, Michael
%A Starostina, Trisha
%A Van Stigt, Daan
%A Rarrick, Spencer
%A Vincent, Sebastian
%A Zhang, Ivan
%A Frosst, Nicholas
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F kocmi-etal-2026-north
%X We present North Small Translate, an open-weight, LLM-based machine translation (MT) model with instruction-following capabilities built on the same foundation as Cohere’s Command A Plus, a mixture-of-experts architecture with 25 billion active parameters out of 218 billion total parameters. North Small Translate is trained using difficulty sampling to obtain challenging documents and a five-step training protocol combining supervised fine-tuning, direct preference optimization, and online reinforcement learning. We prioritized throughput through a non-reasoning base model and supplemented with optional agentic capabilities to unlock translation quality gains. North Small Translate is trained to perform MT-related tasks, including post-editing and quality estimation, as well as related tasks such as general instruction following. The model achieves top MT performance across 50 languages in the class of models under 1T parameters, with no need to run expensive reasoning at inference time.
%U https://aclanthology.org/2026.wmt-main.70/
%P 1305-1317
Markdown (Informal)
[North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)](https://aclanthology.org/2026.wmt-main.70/) (Kocmi et al., WMT 2026)
ACL
- Tom Kocmi, Alexandre Berard, Phil Blunsom, Samuel Cahyawijaya, Shaun Cassini, Ona De Gibert, Aidan Gomez, Nithya Govindarajan, Shun Kiyono, Olivia Lasche, Lawrence Rogers, Kelly Marchisio, Nikita Moghe, Yash More, Camila Moran-Hidalgo, Yiyang Nan, Michael Sachs, Trisha Starostina, Daan Van Stigt, Spencer Rarrick, Sebastian Vincent, Ivan Zhang, and Nicholas Frosst. 2026. North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+). In Proceedings of the Eleventh Conference on Machine Translation, pages 1305–1317, Budapest, Hungary. Association for Computational Linguistics.