Research explores dynamic compression in RNNs for in-context continual learning
lateinteraction · x · 2026-08-21
The post forwards a discussion on in-context continual learning. Traditional RNNs compress an ever-growing history into a fixed-size state, where each token gets a single write operation. The highlighted research investigates "dynamic compression," allowing the model to revisit the past and reorganize its state as it discovers what needs to be reused, thereby accumulating and reusing experience within the same sequence.
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