One loss for pretraining, midtraining and RL: unified training lifts accuracy and coverage at matched compute
Foundation models are usually trained in stages, with both the data and the learning algorithm changing at each one. Unified training writes pretraining, midtraining, supervised fine-tuning and reinforcement learning as maximum-likelihood fitting to target distributions, so static corpora and RL generations mix inside a single trainer, and staged training becomes one special case. In controlled reasoning tasks and a real-world midtraining experiment, it improved the accuracy-compute tradeoff and task coverage while preserving broader capabilities.