Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data

ICLR 2026 Conference Submission20488 Authors

Keywords: Pretraining, Supervised Finetuning, Reasoning, LLM

TL;DR: Dissecting the Synergy of Reasoning Data in Pretraining and SFT

Abstract: The prevailing paradigm for enhancing the reasoning abilities of Large Language Models (LLMs) revolves around post-training on high-quality, reasoning-intensive data. While emerging literature suggests that reasoning data is increasingly incorporated also during the mid-training stage---a practice that is relatively more proprietary and less openly characterized---the role of such data in pretraining remains unclear. In particular, due to the opaqueness of pretraining corpora in most frontier models, the effect of reasoning data introduced at different phases of pre- and/or post-training is relatively less reported in the scientific literature. This raises several important but unsettled questions: Is adding reasoning data earlier during pre-training any better than introducing it during post-training, when the token counts are controlled? Could earlier inclusion risk overfitting and harm generalization, or instead establish durable foundations that later fine-tuning cannot recover? To address these questions, we conduct the first systematic study of how reasoning data—varying in scale, diversity, and quality—affects LLM performance when introduced at different stages of training. Our findings reveal that front-loading reasoning data into pretraining is critical (19% average gain), establishing foundational capabilities that cannot be fully replicated by later-stage SFT, even with more data. We uncover an asymmetric principle for optimal data allocation: pretraining benefits most from broad diversity in reasoning patterns (11% average gain), while SFT is more sensitive to data quality (15% average gain with high quality data). Furthermore, we show that high-quality pretraining data has latent effects, activated only after SFT, and that naively scaling SFT data can be detrimental, washing away the benefits of early reasoning injection. Collectively, our results challenge the conventional separation of language modeling and reasoning, providing a principled guide for strategically allocating data across the entire training pipeline to build more capable models.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 20488