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.
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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