{
  "id": "2610.28871",
  "title": "How Much Gradient Does Truncated Backprop Lose in Looped Transformers?",
  "authors": "Aur Shalev Merin",
  "first_public": "2026-10-20",
  "added_at": "2026-10-22",
  "venue": "arXiv",
  "arxiv": "2610.28871",
  "paper_url": "https://arxiv.org/abs/2610.28871",
  "html_url": "https://arxiv.org/html/2610.28871v1",
  "lens": [
    "recurrent",
    "analysis"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "depth-wise",
    "recurrent"
  ],
  "training": [
    "analysis-only"
  ],
  "questions": [
    "efficiency",
    "scaling",
    "limitations"
  ],
  "evidence": [
    "empirical",
    "mechanistic"
  ],
  "modality": [
    "language"
  ],
  "summary": "Measures how much of the weight gradient is dropped when looped language models backpropagate through only their final iterations. Tracks how this changes during fine-tuning and tests whether the measurement predicts the resulting training loss.",
  "provenance": "Primary-source literature review, 2026-10-22",
  "author_names": [
    "Aur Shalev Merin"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.28871/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-28871",
  "citation_date": "2026-10-20",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.28871/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.28871/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.28871/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.28871/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.28871"
}