Continual learning updates a model or agent as new tasks, feedback and operating conditions appear. In an agent workflow, usage can supply traces and outcomes that reveal what should be evaluated, corrected or added to future training.
A major challenge is avoiding catastrophic forgetting, privacy violations and feedback loops that reinforce mistakes. Safe continual learning separates evidence collection, evaluation and deployment so new behavior is validated before it replaces a known-good system.
How is continual learning different from ordinary model training?
Ordinary training often produces a fixed release from a bounded dataset, while continual learning incorporates new experience across time and changing conditions.
What is catastrophic forgetting in continual learning?
Catastrophic forgetting occurs when learning new information causes a model to lose previously useful capabilities or perform worse on earlier tasks.
