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arXiv · 2601.21624

Training Memory in Deep Neural Networks: Mechanisms, Evidence, and Measurement Gaps

Vasileios Sevetlidis·George Pavlidis
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Abstract

Modern deep-learning training is not memoryless. Updates depend on optimizer moments and averaging, data-order policies (random reshuffling vs with-replacement, staged augmentations and replay), the nonconvex path, and auxiliary state (teacher EMA/SWA, contrastive queues, BatchNorm statistics). This survey organizes mechanisms by source, lifetime, and visibility. It introduces seed-paired, function-space causal estimands; portable perturbation primitives (carry/reset of momentum/Adam/EMA/BN, order-window swaps, queue/teacher tweaks); and a reporting checklist with audit artifacts (order hashes, buffer/BN checksums, RNG contracts). The conclusion is a protocol for portable, causal, uncertainty-aware measurement that attributes how much training history matters across models, data, and regimes.

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Vasileios Sevetlidis, George Pavlidis. 2026-01-29. Training Memory in Deep Neural Networks: Mechanisms, Evidence, and Measurement Gaps. https://arxiv.org/abs/2601.21624

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