Post-training of large language models

Post-training of large language models is a term used in recent technical literature for training applied to a large language model (LLM) after its initial large-scale pretraining. The scope varies among surveys. Supervised fine-tuning or instruction tuning and preference-based alignment are common elements, while broader treatments differ on whether reasoning-focused training, parameter-efficient adaptation, distillation, tool integration, or inference-time scaling are included.

In training-centered usage, a pretrained or base model is further optimized with structured data such as instruction-response pairs, comparisons between candidate responses, step-level judgments, or outcomes that can be checked automatically. Practical systems may combine several stages, for example supervised training followed by preference optimization or reinforcement learning.