North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)

2026-09-12

AI Summary

该研究通过使用难度采样和五步训练协议，开发出一个基于大模型的高效机器翻译系统North Small Translate，以提高50种语言的翻译质量。

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.

Problem

• machine translation
• cost-effective
• high-quality
• multi-lingual

Innovation

• open-weight
• difficulty sampling
• five-step training protocol
• mixture-of-experts architecture