Transitioning from Pre-training to Post-training
Rachit Bansal ⋅ Clara Mohri ⋅ Tian Qin ⋅ Harman Singh ⋅ Samy Jelassi ⋅ Sham Kakade
Abstract
This workshop examines the interaction between pre-training and post-training in modern foundation models. We solicit theoretical, empirical, and methodological work on how pretraining choices determine the success, or failure, of post-training methods including, but not limited to, supervised fine-tuning, RLXF, RLVR, self-improvement, and distillation. We also solicit work studying on how these post- training procedures reshape the base model. The goal is to crystallize the science of the pretraining-to-post-training transition.
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