Training Foundation Models with a Single Algorithm

Foundation models are traditionally trained through a sequential pipeline of distinct phases, including pretraining, fine-tuning, and reinforcement learning. Researchers demonstrate that this algorithmic separation is unnecessary by framing these diverse phases as maximum-likelihood fitting to evolving data targets under a unified training system. This approach integrates static corpora and model-generated responses into a single objective, transforming training design into a strategic problem of data composition, timing, and mixing proportions. Extensive experiments reveal that unified training improves task accuracy, compute efficiency, and generalization while providing a superior starting point for continual learning.