The Shift from Web-Mirroring to Reasoning-Prior Base Models
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the gist
Modern base models are no longer trained as passive mirrors of web text. Instead, they are increasingly built as specialized priors for reinforcement learning, incorporating synthetic reasoning traces and instruction data directly into the pre-training phase.
The Decline of Raw Web Text
The traditional paradigm of training base models as mirrors of the internet is obsolete. While GPT-3 relied on raw web scrapes like Common Crawl for roughly 85% of its training data, modern models have drastically reduced this reliance. For instance, recent models have cut web text to as low as 15% of the total mix, prioritizing code and STEM-related data to better support downstream agentic and reasoning tasks. The goal is no longer to capture a broad snapshot of human knowledge, but to provide the specific atomic skills required for reinforcement learning (RL) to succeed.
Integrating Post-Training Data Early
To ensure stability and performance, developers are pulling instruction-tuning and synthetic data into the pre-training stage. This approach, seen in models like Nemotron-3 Ultra, involves using supervised fine-tuning (SFT) data as a core component of the initial training mix. By exposing the model to the structure of reasoning traces and chat templates early, developers avoid the distribution mismatch that often occurs when a model trained purely on web text is suddenly subjected to RL. This shift also mitigates load-balancing issues in Mixture-of-Experts (MoE) architectures, as the model learns stable expert representations before the intensive RL phase begins.
Synthetic Data and Reasoning Priors
Synthetic data is now a primary tool for scaling and quality control. Techniques like rephrasing seed data allow models to see the same information in multiple formats, which improves representation robustness. Furthermore, training for test-time compute schemes—such as reasoning traces that do not resemble standard human output—is now being introduced during pre-training. This prepares the model to explore effectively during RL, treating the base model as a foundational prior for agentic behavior rather than a general-purpose knowledge repository.