Don't forget, there is more than forgetting: new metrics for Continual Learning

Dev.to / 4/15/2026

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Key Points

The article proposes that evaluating Continual Learning should go beyond simply measuring “forgetting,” using more nuanced metrics that reflect a broader set of behaviors in continual models.
It emphasizes that current evaluation practices may miss important failure modes or trade-offs, motivating the adoption of additional, task-relevant criteria.
The discussion centers on designing and interpreting metrics so they better capture learning progress across time, not just retention of old knowledge.
The goal is to improve how continual learning systems are benchmarked and compared, enabling more reliable assessment of model quality over sequential data streams.

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