Carnegie Mellon research shows unified training method for AI models

A new paper argues pretraining, fine-tuning, and reinforcement learning can run as one algorithm instead of three separate stages Most foundation models are built like a three-course meal: pretraining first, fine-tuning second, reinforcement learning for dessert. A new paper from Carnegie Mellon University suggests the courses might work better served on one plate. The study, titled “Training Foundation Models with a Single Algorithm,” argues that blending all three types of training data into one unified method can beat the traditional staged approach. The paper appeared on arXiv on October 29, 2026.

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