Replay in the Silent Degrees of Freedom: Continual Learning Without an Offline Phase

Abstract:Replay-based continual learning almost always consolidates in a dedicated offline phase or by interleaving replayed samples with the input stream, whereas brains also consolidate during wakefulness through local sleep, brief use-dependent off-periods of individual circuits. We ask whether a network trained by local, biologically constrained rules can consolidate with no offline phase at all. An isolation rule confines replay updates to hidden synapses invisible to the current input under k-winner-take-all dynamics, with optimiser state advanced only inside the mask; a refractory rotation rule makes units that have just fired sit out the next competition, widening the consolidable set; a homeostatic pressure and a relative-novelty gate decide when replay bursts fire and when rotation runs. This inverts the usual direction of non-interfering continual learning: the hidden computation on the current input is held invariant (exactly on the proven channels, and for all but 0.3% of waking samples per update elsewhere) while past memories are written into the degrees of freedom the current batch leaves unused. On class-incremental split-MNIST the system reaches 91.6+-0.3% with no offline phase, at or above the best offline-night schedule on two held-out splits, tied with DER++ and above experience replay, ER-ACE, A-GEM and unmasked local replay; in a single pass it leads DER++ (91.8% against 90.1%) while the night falls to 76.9%. The advantage is largest at small buffers and gives way to the backpropagation references at large ones; on split CIFAR-10 the system leads offline rehearsal and experience replay but trails ER-ACE and DER++. Rotation carries most of the gain; isolation adds the invariance guarantee. The mechanism is not tied to the local rule: under the same schedule a backpropagation network with k-WTA hidden layers gains from rotation, and isolation is again free on top of it.

25 pages, 10 figures. Code: this https URL

Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Machine Learning (stat.ML)

arXiv:2609.31630 [cs.LG]

(or arXiv:2609.31630v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.31630 Focus to learn more arXiv-issued DOI via DataCite

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