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When Is Availability-Aware Training Worth It? A Benchmark and Empirical Study of Interruption-Resilient Optimization Under Predictable Compute Schedules — arXiv:2609.22087

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.22087
License (as archived): CC0-1.0
Archived: 2026-10-09T06:53:09.266Z
SHA-256 of the archived text: 98adfabef7aeb550e3ba02b2a825c28eca05c811f68af120198c19384814e5f8
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How to cite

When Is Availability-Aware Training Worth It? A Benchmark and Empirical Study of Interruption-Resilient Optimization Under Predictable Compute Schedules — arXiv:2609.22087 — arXiv (en), https://arxiv.org/abs/2609.22087 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2dd4e4ecd2aa (Metraipolis archive dc_2dd4e4ecd2aa, sha256 98adfabef7aeb550e3ba02b2a825c28eca05c811f68af120198c19384814e5f8)

BibTeX

@misc{dc2dd4e4ecd2aa,
  title = {When Is Availability-Aware Training Worth It? A Benchmark and Empirical Study of Interruption-Resilient Optimization Under Predictable Compute Schedules — arXiv:2609.22087},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.22087},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2dd4e4ecd2aa; sha256 98adfabef7aeb550e3ba02b2a825c28eca05c811f68af120198c19384814e5f8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.22087. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Training under non-stationary but predictable compute availability (satellites under eclipse, duty-cycled edge devices, power-capped datacenters) is often framed as needing specialized, availability-aware optimizers. We test that premise. We release OrbitTrace, a benchmark of 50 physics-grounded availability traces from SGP4 propagation of live two-line element sets across three orbital regimes, and ask a falsifiable question: when an availability gap interrupts training, is a specialized resumption strategy worth it, or is competent checkpoint-and-resume enough? Our central finding: when optimizer state can be preserved across a gap, the gap is essentially free. A strong checkpoint baseline that restores full optimizer state and indexes its learning-rate schedule in effective (active) time matches uninterrupted training to within data-ordering noise on CIFAR-10/ResNet-18, and exactly on a GPT-2/AdamW task. Advantages previously reported for availability-aware methods, including our own three-pillar method AAT, arise almost entirely from comparison against a weak baseline that indexes its schedule on wall-clock time. Against the strong baseline, reactive adaptation provides no advantage across stationary, optimizer-state-loss, and distribution-drift regimes. We isolate one narrow regime where it helps: for large models whose optimizer state cannot be persisted across gaps and that are interrupted by frequent, short pauses, reconstructing a decayed optimizer moment recovers only ~21% of the state-loss penalty on average (and not robustly across seeds); this vanishes for eclipse-scale gaps, where the decayed moment is indistinguishable from zero. Our contributions are a benchmark, a strong reproducible baseline protocol, and a clear characterization of when interruption-resilient optimization is worth its complexity, and when it is not.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-22 in cs.DC).
Categorie arXiv: Signal Processing (eess.SP) ; Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.22087 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T06:50:13.554Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).