{
  "abstract": "Model choice in backward Q-learning is recursive because a later-stage choice changes the response supplied to an earlier regression and can alter its model-comparison statistic. Separate stagewise criteria do not directly assess the target-stage prediction risk of a completed Q-learning fit. We address this mismatch by treating the entire backward-fitting rule as the unit of comparison and propose feedback-aware soft tuning for sequential multiple assignment randomised trials. Each backward fit uses its own generated responses and is assessed at a common prediction target. The risk criterion retains downstream effects on upstream comparisons, while a separate correction accounts for estimating the final exponential weights from the same observations. For a fixed finite library of smooth recursive maps, we establish an exact risk identity under a Gaussian shift model and an oracle inequality with an explicit adaptation remainder. Under coordinate-representation and moment conditions, these guarantees transfer to prediction risk at any prespecified stage, with the number of stages fixed as sample size increases. A two-stage construction supplies an explicit observable implementation. Numerical studies examine risk estimation and finite-sample performance, and a simulated attention-deficit/hyperactivity-disorder trial illustrates the relation between comparison feedback and treatment recommendations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.12716",
  "author_total": 1,
  "authors": [
    {
      "name": "Masahiro Kojima"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1pknbb8p87ry5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.12716",
  "fulltext_url": null,
  "id": "1pknbb8p87ry5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.12716",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7212618763",
  "pdf_url": "https://arxiv.org/pdf/2609.12716",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1pknbb8p87ry5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:17Z",
      "native_id": "2609.12716",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.12716"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Feedback-Aware Tuning of Recursive Q-Learning",
  "title_text": "Feedback-Aware Tuning of Recursive Q-Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:17Z",
  "url": "https://research.draconitic.com/paper/1pknbb8p87ry5",
  "venue": "arXiv",
  "versions": []
}