The Base Model Is Dead — Varun Singh, Arcee AI

The traditional concept of the "base model," built primarily on massive web-text scrapes to reflect human knowledge, is evolving into a foundation for reasoning and agentic behavior. Modern training paradigms now prioritize reinforcement learning (RL) as a core component rather than a supplementary refinement, with compute budgets increasingly split between supervised learning and RL. This shift necessitates new data strategies, including the integration of synthetic data and post-training datasets directly into the pre-training phase to improve model stability and task-specific performance. As models gain proficiency in code and STEM, the reliance on general web text is diminishing, replaced by specialized data that prepares models for complex, real-world interactions. Ultimately, supervised learning now functions as a mechanism to build the atomic skills necessary for effective RL, fundamentally altering how developers approach model architecture and training recipes.