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A recent paper worth noting proposes a “unified training” algorithm that integrates pre-training, mid-training, and post-training into a single algorithmic workflow, simplifying infrastructure and providing a framework for better scientific research.
A CMU-led paper introduces 'unified training,' showing that pretraining, midtraining, SFT, and RL can be collapsed into a single likelihood-based algorithm where RL objectives are treated as maximum-likelihood fitting to model-generated targets, outperforming staged training in several accuracy-compute and continual-learning experiments.
Carnegie Mellon paper shows that pretraining, fine-tuning, and RL can collapse into a single training rule (MaxRL as likelihood training on correct answers), with blended data runs outperforming staged training on reasoning and coding benchmarks while preserving general skills.
A paper unifying foundation-model training stages (pretraining, midtraining, SFT, RL) under a single likelihood-based algorithm that blends static data and RL generations, improving accuracy-compute tradeoff and capability retention.