Building Multilingual Bridges 2026 09 10

Building Multilingual Bridges Skip to content AI for Empowerment: Your freedom. Your focus. See how AI gives you more time for what truly moves you. Explore now

Products

Solutions

Resources

Blog

Research

Company

Sign in

Request a demo

Platform North

Enterprise-ready AI for business

Compass

Intelligent search and discovery

Models Command

Generative language models

Transcribe New

Speech recognition model

North Small Translate NEW

Machine translation model

Parse New

Document parsing model

Embed

Semantic representation model

Rerank

Retrieval optimization model

Models Overview

Product Products Overview

Total Cost of AI Ownership

Pricing

Featured Command: High-performance generative AI models for real-world applications

Deploy Model Vault

Dedicated model inference platform

Private Deployments

On-prem or isolated VPCs

Security

Protect your data at every stage

See deployment options

By Industry Financial Services

Public Sector

Technology

Telecommunications

Energy and Utilities

Healthcare and Life Sciences

Manufacturing

Featured Model Vault provides fully-isolated, performant inference with Saas simplicity

Insights Customer Stories

For Developers Developers

Models Overview

Docs

Discord

LLM University

Connect Partners

Events

Webinars

Merch Store

Featured How CoreWeave used Cohere North to transform its customer support in 90 days

Blog

The latest news, launches, and insights

Read more

The state of sovereign AI adoption in 2026

Cohere and the University of Waterloo launch partnership to strengthen Canada’s AI talent pipeline

Introducing North Automations: Intelligent workflow orchestration

Research Cohere Labs

Cohere’s ML research lab

Explorations Future(s) of Work

How will AI change the way we work?

Aya Models

Multilingual AI at scale

All Papers

Initiatives Research Scholars

Finding the new generation of ML talent

Open Science Community

Championing global, open science

Catalyst Grants

Supporting impactful ML endeavors

Resources Blog

Hugging Face

Events

Featured The future of work debate has an evidence problem

About

Careers

Newsroom

Sep 10, 2026 Building Multilingual Bridges Data Mixing as the Pillar of Generalization for In-Language Reasoning

Optimized data mixtures including multilingual reasoning, non-reasoning and English reasoning data yield superior in-language reasoning. We build Tiny Aya L2-Thinker, a RLM supporting in-language reasoning on over 60 languages without sacrificing performance, the broadest coverage to-date for multilingual RLMs.

Read the paper

Authors

Mehrnaz Mofakhami, Ananya Sahu, Alejandro R. Salamanca, Daniel D'souza, Alexandre Bérard, Thomas Euyang, Marzieh Fadaee, Julia Kreutzer

Abstract

Reasoning language models have substantially advanced on a variety of complex tasks, yet the capability remains overwhelmingly English-centric: even when prompted in another language, models predominantly reason in English. This is inaccessible for non-English-speaking users, risks losing the intent of the original question, and forgoes knowledge more readily expressed in the target language. In this work, we advance L2 reasoning , the ability of a model to reason consistently in the language of the user's prompt, thus building an in-language bridge between the prompt and the answer. We approach this problem from a data-centric angle, investigating how to optimize data composition and scheduling in SFT for reasoning generalization. Building on top of a massively multilingual 3.35B base model, we achieve above 95% L2 reasoning rate across 60 languages on 5 benchmarks spanning math, commonsense reasoning, instruction following and open-ended generation, while keeping performance strong. We show the path to generalizing L2 reasoning to held-out languages goes through broader language coverage, readily available multilingual non-reasoning data, and a sufficient English reasoning backbone. These findings indicate that reasoning is a language-agnostic behavior that can be transferred across typologically diverse languages through careful data mixing and without requiring reasoning supervision in every target language. We release our model weights and multilingual reasoning data to support further research on accessible, in-language reasoning.

Reasoning multilingual

Related works

Research CALIBER: Calibrating confidence before and after reasoning in language models

Read

Research The IOL-AI Challenge: An Open Challenge towards Advancing Linguistic Reasoning

Read

Research Findings of the WMT25 Multilingual Instruction Shared Task: Persistent Hurdles in Reasoning, Generation, and Evaluation

Read