RECALL: A simple way to stop AI from forgetting what it learned — without old data
Researchers found a way to help big language AIs keep learning new things without erasing old skills, and its called RECALL.
The idea is to look at what the model sees inside, compare those inner patterns across versions, then gently merge them so the best parts stay.
It keep general skills in early layers and lets deeper parts change for new jobs, so the model not just copy, it adapt.
This works even when you have no old data to retrain with, which is handy for growing systems.
Tests across many language tasks show better memory and wider use than older tricks, so models forget less and handle new tasks better.
It feels like patching a brain so new memories don't wipe old ones.
The method is made to scale for large models and to be used without heavy data sharing, making it useful for real world setups that change fast, and you get a model that keeps knowledge while still learning more.
Read article comprehensive review in Paperium.net:
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation viaHierarchical Model Merging
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