Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models

New theoretical research analyzes how Large Language Models learn during pretraining versus post-training phases, revealing that balanced pretraining data creates latent capabilities activated later, while supervised fine-tuning works best on small, challenging datasets and reinforcement learning requires large-scale data that isn't overly difficult.

• →Balanced pretraining data can induce latent capabilities that are later activated during post-training phases.
• →Supervised fine-tuning (SFT) learns most effectively from small sets of examples that challenge the pretrained model.
• →Excessively large SFT datasets may actually dilute informative pretraining signals and reduce performance.
• →Reinforcement learning works best on large-scale datasets that are not overly difficult for the pretrained model.
• →The research provides theoretical framework explaining why different training phases require different data strategies.