{
  "id": "2609.37432",
  "title": "Looped Actor: Depth-Recurrent Reasoning Models for Reinforcement Learning",
  "authors": "T. Konstantin Rusch, Tim Seyde, Jared Boyer, Zach J. Patterson, Daniela Rus",
  "first_public": "2026-09-29",
  "added_at": "2026-10-18",
  "venue": "arXiv",
  "arxiv": "2609.37432",
  "paper_url": "https://arxiv.org/abs/2609.37432",
  "lens": [
    "recurrent"
  ],
  "representation": [
    "hidden-state"
  ],
  "computation": [
    "recurrent",
    "depth-wise"
  ],
  "training": [
    "reinforcement-learning"
  ],
  "questions": [
    "architecture",
    "controllability",
    "efficiency",
    "theory"
  ],
  "evidence": [
    "formal",
    "empirical"
  ],
  "modality": [
    "symbolic"
  ],
  "summary": "Uses a shared transformer block to refine a reinforcement-learning policy's internal state before choosing an action. Learns to vary the number of loops with the current state, and studies adaptive computation in puzzles and robotic-control tasks.",
  "provenance": "Primary-source literature search, 2026-10-18",
  "author_names": [
    "T. Konstantin Rusch",
    "Tim Seyde",
    "Jared Boyer",
    "Zach J. Patterson",
    "Daniela Rus"
  ],
  "record_url": "https://latent-implicit-thinking.github.io/papers/2609.37432/",
  "legacy_record_url": "https://latent-implicit-thinking.github.io/papers/#paper-2609-37432",
  "citation_date": "2026-09-29",
  "json_url": "https://latent-implicit-thinking.github.io/papers/2609.37432/record.json",
  "markdown_url": "https://latent-implicit-thinking.github.io/papers/2609.37432/index.md",
  "bibtex_url": "https://latent-implicit-thinking.github.io/papers/2609.37432/citation.bib",
  "csl_url": "https://latent-implicit-thinking.github.io/papers/2609.37432/citation.csl.json"
}