Unified training proposes one algorithm for every foundation model training stage

On Oct. 30, @yus167 introduced unified training as a proposed algorithm spanning pretraining, midtraining and post-training. @Zanette_ai describes extending a maximum-likelihood approach to reinforcement-learning objectives, shifting attention to data design: what data models see, in what proportions and in what order. @Zanette_ai reports experimental gains in accuracy, coverage and compute efficiency over conventional staged training, while @yus167 characterizes the team's comparisons as preliminary.