{
  "id": "2610.41720",
  "title": "UpLoop: Training Looped Transformers via Upcycling at No Extra Pretraining Compute",
  "authors": "Cheng Liang, Tengfei Zhang, Ya Zhang, Yanfeng Wang, Zhouhan Lin",
  "first_public": "2026-10-30",
  "added_at": "2026-11-02",
  "venue": "arXiv",
  "arxiv": "2610.41720",
  "paper_url": "https://arxiv.org/abs/2610.41720",
  "html_url": "https://arxiv.org/html/2610.41720v1",
  "lens": [
    "recurrent"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise"
  ],
  "training": [
    "pretraining",
    "curriculum"
  ],
  "questions": [
    "architecture",
    "efficiency",
    "scaling"
  ],
  "evidence": [
    "empirical",
    "scaling"
  ],
  "modality": [
    "language"
  ],
  "summary": "Pretrains a shallow transformer, grows it by copying layers and optimizer states, then adds shared-weight repetitions of its middle block. Uses the compute saved early in training to match a dense baseline in parameters, training tokens, and total training FLOPs.",
  "provenance": "Primary-source literature review, 2026-11-02",
  "author_names": [
    "Cheng Liang",
    "Tengfei Zhang",
    "Ya Zhang",
    "Yanfeng Wang",
    "Zhouhan Lin"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.41720/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-41720",
  "citation_date": "2026-10-30",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.41720/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.41720/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.41720/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.41720/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.41720"
}