Post-training begins with a base model and applies methods such as supervised fine-tuning, preference optimization, reinforcement learning, or knowledge distillation. It can teach stronger task behavior without repeating the full cost of training the model from the beginning.
The process can create specialist checkpoints and then combine their strengths into a more general release. Results depend on training data, objectives, evaluation coverage, and whether gains in one area reduce useful behavior elsewhere.
ELI5
Post-training is the additional teaching an AI model receives after its broad initial training. It focuses the model on desired tasks, styles, safety behavior, or quality improvements.
For example, developers can train separate versions for mathematics, coding, and agent tasks, then use their outputs to improve one combined model. Careful testing checks that the improvement did not create new weaknesses.
How is post-training different from pretraining?
Pretraining learns broad patterns from large datasets, while post-training adapts that base model toward selected tasks and behavior.
What methods are used in post-training?
Methods include supervised fine-tuning, preference optimization, reinforcement learning, distillation, and task-specific adaptation.
