On Tiny Episodic Memories in Continual Learning

Tiny Memories, Big Gains in Continual Learning

Imagine a program that must learn new skills one after another, without forgetting the old ones.
Researchers found that saving a tiny episodic memory — just a few past examples — and mixing them while training new tasks helps the system keep what it learned.
It was surprising: even when each example is seen only once, simple joint training with replay beats more complex setups.
The trick: repeat a handful of old examples alongside new data, and the model actually generalize better, not worse.
Tests on different problems show consistent wins, with boosts around 7–17% better when the memory uses a single example per class.
That means less supervision, less fuss, and a light memory that still keeps skill.
It feels simple, yet weirdly powerful, and it will change how we build learning systems.
Try to picture phones or robots that learn all day but still remember — small memory, big difference.
it's a small step, but big promise for smarter machines that remember more with very little data.

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On Tiny Episodic Memories in Continual Learning

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