Continuous Post-Training (2025) › The Problem

A fixed model develops two kinds of mismatch. Its factual associations age, and its behavior may not fit the way people use it. For example, an old model may recommend a deprecated library API. Deployment logs may also reveal a recurring failure on a prompt pattern that was rare in the original evaluation set.

Updating all weights can be expensive. It can also damage unrelated behavior when the update set overrepresents one task. This failure is usually called catastrophic forgetting: training on the new distribution improves the target task while reducing performance on earlier ones.

The problem is not solved by training more frequently. Each candidate still needs traceable data, regression tests, and a release decision. Without those controls, frequent training can make failures harder to reproduce because two deployments may use different model versions.

Different defects also call for different interventions. Retrieval is often suitable for changing facts. A small adapter may teach a stable output format. A safety-policy change may require preference data and broader adversarial evaluation. Architectural limits cannot be repaired by attaching another adapter.