Minibatch persistency, eight years later: what batch reuse costs in steps and joules, and what it saves in data
Minibatch persistency reuses data instead of reading it: rather than drawing a fresh minibatch at every optimizer step, it takes K consecutive steps on the same one. Absorbed into data echoing in 2019, it has carried one objection -- that reuse merely imitates a larger learning rate -- and no baseline tuned as carefully as the method itself. This paper runs the missing test. A pre-registered study trains a 49M-parameter Transformer on FineWeb-Edu at minibatch size B in {32, 128, 512}, 8 seeds per cell, tuning the learning rate separately for every batch size and every arm, against a reuse-free control that changes the sampling and nothing else. Each headline claim is a cost to reach a fix...
| Authors | Matteo Fischetti |
|---|---|
| Primary category | cs.LG |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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