AI model post-training changes model behavior after pretraining. Techniques can include supervised fine-tuning, preference optimization, reward-based learning and other forms of adaptation using curated task examples or feedback.
Operational traces can make post-training more relevant to a specific business, but they may contain sensitive data and correlated mistakes. Teams need consent, provenance, redaction, evaluation and separation between training examples and independent test cases.
ELI5
Model post-training teaches or adjusts an AI model after its broad initial training is complete. It uses selected examples, preferences, rewards or domain data to make the model behave better for particular tasks.
For example, a general model can receive reviewed customer-support examples so it follows a company's response policy more reliably. The evaluation set should remain separate, and sensitive information in training examples must be handled carefully.
How is post-training different from pretraining?
Pretraining learns broad patterns from large datasets, while post-training adapts behavior using narrower examples, preferences, rewards or domain-specific data.
Can private operational data be used for model post-training?
Yes, when authorized and protected, but organizations must manage privacy, rights, security, provenance and independent evaluation carefully.
