Reasoning Gains Persist When Models Learn Them During Pretraining
Shrimai Prabhumoye of Mistral AI used a Stanford CS25 seminar to argue that large-language-model pretraining is becoming less a matter of adding tokens and more a question of training strategy. Drawing on studies of curriculum ordering, early reasoning data, and reinforcement as a pretraining objective, she said base models improve when they see broad data before high-quality data, encounter reasoning traces during pretraining rather than only post-training, and are rewarded for intermediate thoughts that improve prediction.
Stanford Online·May 11, 2026·17 min read
