Continual learning is the process of updating a system as new experience arrives. Instead of treating a model or learned state as permanently fixed, an application can run training tasks, inspect their progress, and compare the resulting behavior.
It is different from remembering a document. Storing a new record makes information available; training changes the behavior of a learned component. Choose the operation that matches your goal.
Make a change measurable
Define the task, inputs, expected improvement, and evaluation before starting a learning job. Keep a stable comparison set so a new result can be compared with the previous one. Record configuration and model identity alongside the outcome.
A useful evaluation asks both whether the new task improved and whether older behavior regressed. A lower training loss alone may not answer either question for your application.
Recovery must be real
Checkpoints and rollback are valuable only when they actually save and restore the relevant state. The current daemon's checkpoint-status and recovery handlers acknowledge requests without implementing restoration, and no checkpoint-creation HTTP route is registered in the audited snapshot. Do not design a production recovery procedure around those acknowledgments.
Use the continual API for available task contracts and backup guidance to distinguish data backups from model or training checkpoints.