{
  "id": "2610.26030",
  "title": "Truncate or Not? A Scaling Study of Looped Transformers with Truncated Backpropagation",
  "authors": "Xuyuan Xiong, Siyu Chen, Zihan Shao, Yue Su, Weiyang Jin, Ping Luo, Zhuoran Yang, Xihui Liu",
  "first_public": "2026-10-19",
  "added_at": "2026-10-22",
  "venue": "arXiv",
  "arxiv": "2610.26030",
  "paper_url": "https://arxiv.org/abs/2610.26030",
  "html_url": "https://arxiv.org/html/2610.26030v2",
  "lens": [
    "recurrent",
    "analysis"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise"
  ],
  "training": [
    "pretraining",
    "analysis-only"
  ],
  "questions": [
    "scaling",
    "efficiency"
  ],
  "evidence": [
    "empirical",
    "scaling"
  ],
  "modality": [
    "language"
  ],
  "summary": "Measures how forward loop counts and truncated backpropagation affect language-model pretraining across model sizes and data budgets. Fits scaling laws to compare full backpropagation through fewer loops with truncated alternatives at matched compute.",
  "provenance": "Primary-source literature review, 2026-10-22",
  "author_names": [
    "Xuyuan Xiong",
    "Siyu Chen",
    "Zihan Shao",
    "Yue Su",
    "Weiyang Jin",
    "Ping Luo",
    "Zhuoran Yang",
    "Xihui Liu"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.26030/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-26030",
  "citation_date": "2026-10-19",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.26030/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.26030/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.26030/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.26030/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.26030"
}