{
  "abstract": "The current deep learning training paradigm employs end-to-end backpropagation, regardless of the training stage, i.e. pre-training or fine-tuning. However, backpropagating through the entire model is neither biologically plausible nor memory efficient, since learning inside the brain is highly localized. Therefore, we propose LocalProp, a training procedure that locally updates the weights of a model. Our neuro-localized weight updates follow the \"pre-training then fine-tuning\" paradigm, where the pre-training is based on I-JEPA. After locally updating the weights, a pruning operation is performed, followed by a short final fine-tuning phase. Pruning helps by sending the learning signal from higher blocks to lower blocks. We perform experiments on several datasets, including large-scale benchmarks such as ImageNet, and empirically show that LocalProp reaches good performance at a fraction of GPU peak memory. By varying the number of jointly optimized blocks, we identify gradient-propagation span as a practical control over the accuracy-memory trade-off.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.32517",
  "author_total": 3,
  "authors": [
    {
      "name": "Diana-Nicoleta Grigore"
    },
    {
      "name": "Iuliana Georgescu"
    },
    {
      "name": "Radu Tudor Ionescu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0hgj57p5j8dm9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.32517",
  "fulltext_url": null,
  "id": "0hgj57p5j8dm9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.32517",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ]
  ],
  "openalex": "W7214762229",
  "pdf_url": "https://arxiv.org/pdf/2609.32517",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0hgj57p5j8dm9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:17Z",
      "native_id": "2609.32517",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.32517"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "LocalProp: Neuro-Localized Memory-Efficient Backpropagation",
  "title_text": "LocalProp: Neuro-Localized Memory-Efficient Backpropagation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:17Z",
  "url": "https://research.draconitic.com/paper/0hgj57p5j8dm9",
  "venue": "arXiv",
  "versions": []
}