Continual Learning Trainer

Software componentModel AdaptationModelsarc:ContinualLearningTrainer

A training component that updates a learned model on new experience while rehearsing sampled past experience and constraining gradients so performance on earlier tasks is preserved.

Responsibility. Trains models on new tasks without degrading previously acquired skills.

Also known as: Replay-based trainer, Offline replay learner, Periodic retraining on recent data

Direct neighbourhood (hover for relationship types)

Relationships

reads dependency

Experience Replay Buffer Ch5.7

is triggered by dynamic

Data Drift Detector Ch9.8 Ch10.2

trains lifecycle

Design guidance

SHOULD mix a share of older experience into every training batch when learning new skills.
MAY project gradients that would increase loss on earlier tasks, trading slightly slower new learning for retention.

Quantitative guidance

As stated by the sources; verify before use.

Mini-batch of 32 episodes sampled 80% new-skill / 20% old-skill (Ch5.7).
With replay: 90% box-handling and 92% small-object success (vs. 95% baseline); without replay small-object success drops to 30-35% (Ch5.7).

Classification

Patterns: Experience replayGradient Episodic Memory (GEM) gradient projectionScheduled low-traffic replay
Quality attributes: Interaction capability (ISO/IEC 25010)Flexibility (ISO/IEC 25010)
Risks mitigated: Catastrophic forgetting

Sources

Ch5.7: T. Nguyen, "Episodic Memory," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.7. ISBN: 9798244538229.
Ch9.8: T. Nguyen, "Standards and Frameworks for AI Governance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.8. ISBN: 9798244538229.
Ch10.2: T. Nguyen, "Proactive Agents," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.2. ISBN: 9798244538229.