Continuous Post-Training (2025) › Applications and Impact
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The clearest use case is a stable, repeated error for which a team can collect representative examples. Suppose a tool-using model repeatedly chooses the wrong API operation for one class of request. An adapter can target that decision, while regression sets check that other tool calls still work.
Post-training can also adjust output conventions or domain behavior. A support model might need to follow a revised escalation policy. A coding model might need examples from a newly adopted internal API. These cases have a bounded desired behavior, which makes update data and acceptance criteria easier to define.
Fresh factual lookup is less suitable. Encoding today's interest rate or package version into weights provides no guarantee that the model will recall it correctly tomorrow. Retrieval from a maintained source is usually easier to inspect and update. Post-training may still teach the model how to use that source or when to decline an unsupported answer.
Shorter training runs make experiments cheaper, but they do not make feedback automatically trustworthy. Usage logs overrepresent active users and frequent tasks. A team must account for consent, privacy, sampling bias, and malicious submissions before turning logs into training examples.