Continuous Post-Training (2025) › What Continuous Post-Training Changed
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By 2025, post-training was no longer best understood as one final step between pretraining and release. Teams could maintain a base checkpoint and ship smaller behavioral revisions around it. PEFT made those revisions cheaper to train and store, while continual-learning research supplied ways to measure interference.
The lasting change was operational. Model development acquired recurring release concerns familiar from software systems: versioned artifacts, regression gates, staged deployment, and rollback. Models still differ from ordinary software because their behavior cannot be enumerated in advance, so those controls reduce risk without eliminating it.
The central design question is therefore not "How can this model learn continuously?" It is "Which change should enter the weights, and what evidence is enough to release it?" That distinction keeps rapidly changing facts in inspectable data systems while reserving post-training for behavior that merits a model update.