{
  "id": "2610.25612",
  "title": "Curriculum Learning Teaches LLMs Long-Horizon Implicit Reasoning, Up To A Limit",
  "authors": "Jimson Huang, Maxwell Nickell, Ruowang Zhang, Abulhair Saparov",
  "first_public": "2026-10-19",
  "added_at": "2026-10-22",
  "venue": "arXiv",
  "arxiv": "2610.25612",
  "paper_url": "https://arxiv.org/abs/2610.25612",
  "html_url": "https://arxiv.org/html/2610.25612v1",
  "lens": [
    "analysis"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "depth-wise"
  ],
  "training": [
    "curriculum",
    "supervised"
  ],
  "questions": [
    "capability",
    "scaling",
    "generalization",
    "limitations"
  ],
  "evidence": [
    "empirical",
    "scaling",
    "boundary"
  ],
  "modality": [
    "language",
    "symbolic"
  ],
  "summary": "Trains models to select the next step of a proof without writing intermediate reasoning, gradually increasing required lookahead and the number of attribute labels. Studies how training compute, model size, initialization, and curriculum steps affect learned lookahead and transfer to other reasoning tasks.",
  "provenance": "Primary-source literature review, 2026-10-22",
  "author_names": [
    "Jimson Huang",
    "Maxwell Nickell",
    "Ruowang Zhang",
    "Abulhair Saparov"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2610.25612/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2610-25612",
  "citation_date": "2026-10-19",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2610.25612/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2610.25612/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2610.25612/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2610.25612/citation.csl.json",
  "pdf_url": "https://arxiv.org/pdf/2610.25612"
}