Understanding Reasoning from Pretraining to Post-Training

A study on Hugging Face Papers maps how reasoning capabilities emerge across pretraining and post-training phases in LLMs. The analysis shows that specific reasoning skills are learned or reinforced differently in each phase.

Understanding which phase contributes most to reasoning allows operators to allocate compute resources more efficiently. Post-training techniques (RLHF, fine-tuning) may be optimized to compensate for reduced pretraining scale, shifting the cost structure of training. This separates reasoning gains from blind scaling.

Builders can reduce pretraining compute budgets if targeted post-training interventions can induce equivalent reasoning improvements, lowering total training cost. Workflows that treat post-training as an optional or secondary step become obsolete; integrated pipelines co-optimizing both phases will be standard. Second-order effect: demand for high-quality post-training data and reward modeling infrastructure rises relative to raw web-scale data collection.