{
  "id": "2502.08482",
  "title": "Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning",
  "first_public": "2025-02-12",
  "paper_url": "https://arxiv.org/abs/2502.08482",
  "authors": "Qifan Yu, Zhenyu He, Sijie Li, Xun Zhou, Jun Zhang, Jingjing Xu, Di He",
  "arxiv": "2502.08482",
  "added_at": "2026-11-09",
  "venue": "arXiv",
  "provenance": "Primary-source literature review, 2026-11-09",
  "html_url": "https://arxiv.org/html/2502.08482v1",
  "lens": [
    "recurrent",
    "beyond-sequential"
  ],
  "representation": [
    "hidden-state",
    "text-latent-hybrid"
  ],
  "computation": [
    "recurrent",
    "parallel"
  ],
  "training": [
    "supervised",
    "distillation"
  ],
  "questions": [
    "architecture",
    "generalization"
  ],
  "evidence": [
    "empirical"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Trains a looped transformer to predict the tokens of a reasoning step in parallel at each iteration. Uses its longer reasoning traces to train an autoregressive model, testing length generalization on arithmetic and dynamic-programming tasks.",
  "author_names": [
    "Qifan Yu",
    "Zhenyu He",
    "Sijie Li",
    "Xun Zhou",
    "Jun Zhang",
    "Jingjing Xu",
    "Di He"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2502.08482/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2502-08482",
  "citation_date": "2025-02-12",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2502.08482/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2502.08482/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2502.08482/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2502.08482/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2502.08482"
}