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      "statement": "# Lean-STaR: retrospective thought augmentation for tactic prediction\n\nGenerated informal rationales retrospectively from ground-truth tactics, trained a model to interleave thoughts and tactics, and improved it through verified-proof expert iteration.\n\n## Scientific scope\n\nInherits STaR, Mathlib, tactic prediction and expert iteration. Generated rationales are training artifacts, not guaranteed faithful internal reasoning or standalone mathematical proofs. Benchmark gains are for specified base models and finite pass budgets.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nHaohan Lin; Zhiqing Sun; Yiming Yang; Sean Welleck\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Lean-STaR: Learning to Interleave Thinking and Proving](https://arxiv.org/abs/2407.10040v1)\n",
      "summary": "Generated informal rationales retrospectively from ground-truth tactics, trained a model to interleave thoughts and tactics, and improved it through verified-proof expert iteration.",
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    "title": "Lean-STaR: retrospective thought augmentation for tactic prediction",
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