Magistral
We introduce Magistral, Mistral's first reasoning model and our own scalable
reinforcement learning (RL) pipeline. Instead of relying on existing
implementations and RL traces distilled from prior models, we follow a ground
up approach, relying solely on our own models and infrastructure. Notably, we
demonstrate a stack that enabled us to explore the limits of pure RL training
of LLMs, present a simple method to force the reasoning language of the model,
and show that RL on text data alone maintains most of the initial checkpoint's
capabilities. We find that RL on text maintains or improves multimodal
understanding, instruction following and function calling. We present Magistral
Medium, trained for reasoning on top of Mistral Medium 3 with RL alone, and we
open-source Magistral Small (Apache 2.0) which further includes cold-start data
from Magistral Medium.
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Mistral-AI
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Abhinav Rastogi
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Albert Q. Jiang
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Andy Lo
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Gabrielle Berrada
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Guillaume Lample
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Jason Rute
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Joep Barmentlo
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Karmesh Yadav
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Kartik Khandelwal
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Khyathi Raghavi Chandu
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Léonard Blier
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Lucile Saulnier
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Matthieu Dinot
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Maxime Darrin
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Neha Gupta
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Roman Soletskyi
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Sagar Vaze
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Teven Le Scao
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Yihan Wang
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Adam Yang
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Alexander H. Liu
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Alexandre Sablayrolles
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Amélie Héliou
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Amélie Martin
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Andy Ehrenberg
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Anmol Agarwal
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Antoine Roux
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Arthur Darcet
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Arthur Mensch
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Baptiste Bout
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Baptiste Rozière
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Baudouin De Monicault
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Chris Bamford
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Christian Wallenwein
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Christophe Renaudin
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Clémence Lanfranchi
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Darius Dabert
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Devon Mizelle
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Diego de las Casas
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Elliot Chane-Sane
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Emilien Fugier
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Emma Bou Hanna
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Gauthier Delerce
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Gauthier Guinet
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Georgii Novikov
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Guillaume Martin
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Himanshu Jaju
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Jan Ludziejewski
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Jean-Hadrien Chabran
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Jean-Malo Delignon
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Joachim Studnia
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Jonas Amar
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Josselin Somerville Roberts
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Julien Denize
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Karan Saxena
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Kush Jain
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Lingxiao Zhao
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Louis Martin
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Luyu Gao
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Lélio Renard Lavaud
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Marie Pellat
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Mathilde Guillaumin
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Mathis Felardos
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Maximilian Augustin
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Mickaël Seznec
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Nikhil Raghuraman
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Olivier Duchenne
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Patricia Wang
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Patrick von Platen
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Patryk Saffer
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Paul Jacob
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Paul Wambergue
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Paula Kurylowicz
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Pavankumar Reddy Muddireddy
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Philomène Chagniot
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Pierre Stock
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Pravesh Agrawal
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Romain Sauvestre
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Rémi Delacourt
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Sanchit Gandhi
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2025-07-21T08:30:48.603Z
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Sanchit Gandhi
user
sanchit-gandhi
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Sandeep Subramanian
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Shashwat Dalal
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Siddharth Gandhi
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Soham Ghosh
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Srijan Mishra
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Sumukh Aithal
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Szymon Antoniak
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Thibault Schueller
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Thibaut Lavril
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Thomas Robert
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Thomas Wang
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Timothée Lacroix
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Valeriia Nemychnikova
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Victor Paltz
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Virgile Richard
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Wen-Ding Li
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William Marshall
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Xuanyu Zhang
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Yunhao Tang
Magistral
We introduce Magistral, Mistral's first reasoning model and our own scalable
reinforcement learning (RL) pipeline. Instead of relying on existing
implementations and RL traces distilled from prior models, we follow a ground
up approach, relying solely on our own models and infrastructure. Notably, we
demonstrate a stack that enabled us to explore the limits of pure RL training
of LLMs, present a simple method to force the reasoning language of the model,
and show that RL on text data alone maintains most of the initial checkpoint's
capabilities. We find that RL on text maintains or improves multimodal
understanding, instruction following and function calling. We present Magistral
Medium, trained for reasoning on top of Mistral Medium 3 with RL alone, and we
open-source Magistral Small (Apache 2.0) which further includes cold-start data
from Magistral Medium.
Magistral, a scalable reinforcement learning pipeline, demonstrates that RL can enhance multimodal understanding and instruction following in large language models without requiring existing RL traces.
reinforcement learning RL LLMs multimodal understanding instruction following function calling cold-start data