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[Submitted on 13 Oct 2026] Title:Data Density Scaling Laws for Image Self-Distillation View a PDF of the paper titled Data Density Scaling Laws for Image Self-Distillation, by Alvard Barseghyan and 3 other authors View PDF HTML (experimental) Abstract:Current scaling law formulations suggest that increasing the number of unique samples in a dataset improves model performance, while repeated exposure to the same samples provides diminishing value. In this work, we examine this assumption for image self-distillation models. Through systematic experiments across diverse datasets, namely Maxar, SATLAS, Waymo, nuPlan, Ego4D, ImageNet, and YFCC15M, we demonstrate that the effects of sample repetition on downstream performance are non-trivial and dataset-dependent. We identify settings where repeatedly training on a smaller random subset can outperform training on a larger set of unique samples. This finding highlights a limitation of existing scaling laws. To address this gap, we propose a modified version of a Chinchilla-style scaling law that explicitly accounts for two factors: the number of unique samples and repetition per sample. This formulation provides a framework for understanding data-compute trade-offs and improves prediction in some regimes where repetition plays a central role, although its benefits are not consistent across datasets. Finally, we examine how subset selection and augmentation affect downstream performance. Comments: 25 pages, 11 figures Subjects: Machine Learning (cs.LG) Cite as: arXiv:2610.19013 [cs.LG] (or arXiv:2610.19013v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.19013 arXiv-issued DOI via DataCite (pending registration) Submission history From: Hrant Khachatrian [view email] [v1] Tue, 13 Oct 2026 18:08:51 UTC (9,817 KB) Full-text links: Access Paper: View a PDF of the paper titled Data Density Scaling Laws for Image Self-Distillation, by Alvard Barseghyan and 3 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.LG new | recent | 2026-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)