{
  "id": "2610.38422",
  "title": "Deeper Backpropagation or More Updates? Training Looped Language Models Across Budgets",
  "authors": "Jiarun Shen, Xiao Li",
  "first_public": "2026-10-28",
  "added_at": "2026-11-02",
  "venue": "arXiv",
  "arxiv": "2610.38422",
  "paper_url": "https://arxiv.org/abs/2610.38422",
  "html_url": "https://arxiv.org/html/2610.38422v1",
  "lens": [
    "recurrent",
    "analysis"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise"
  ],
  "training": [
    "pretraining",
    "analysis-only"
  ],
  "questions": [
    "efficiency",
    "scaling",
    "evaluation"
  ],
  "evidence": [
    "empirical",
    "scaling"
  ],
  "modality": [
    "language"
  ],
  "summary": "Compares short and long backpropagation windows in looped language models at matched token and training-time budgets. Extra updates favor shorter windows at small budgets, but that advantage shrinks as training grows.",
  "provenance": "Primary-source literature review, 2026-11-02",
  "author_names": [
    "Jiarun Shen",
    "Xiao Li"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.38422/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-38422",
  "citation_date": "2026-10-28",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.38422/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.38422/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.38422/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.38422/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.38422"
}