Post-training in production is about engineering discipline around data mixtures and yield metrics, not algorithmic breakthroughs—small improvements in converting training data into usable supervision can compound into significant model gains.
This paper treats LLM post-training as industrial maintenance work, not research. Teams inherit a deployed model and must improve it within strict compute budgets without breaking existing capabilities.