{
  "schema_version": 1,
  "id": "evt-2024-deepseek-prover-v1",
  "slug": "deepseek-prover-synthetic-lean-data",
  "title": {
    "en": "DeepSeek-Prover scales Lean theorem proving with millions of synthetic formal statements",
    "zh-CN": "DeepSeek-Prover 用数百万合成形式命题扩展 Lean 定理证明"
  },
  "dates": {
    "event": "2024-05-23",
    "first_public_claim": "2024-05-23",
    "paper_release": "2024-05-23",
    "artifact_release": null,
    "independently_verified": null,
    "added_to_chronicle": "2026-09-10",
    "last_updated": "2026-09-10"
  },
  "event_types": [
    "proof",
    "system",
    "dataset"
  ],
  "significance": {
    "tier": "H2",
    "rationale": {
      "en": "DeepSeek-Prover demonstrated that large-scale synthetic Lean data could materially raise whole-proof generation performance.",
      "zh-CN": "DeepSeek-Prover 展示了大规模合成 Lean 数据能够显著提升整段形式证明生成能力。"
    }
  },
  "mathematical_novelty": {
    "type": "benchmark_result"
  },
  "interfaces": [
    "formal",
    "symbolic"
  ],
  "claim": {
    "en": "DeepSeek-Prover trained on a synthetic corpus containing about eight million formal statements with proofs and reported 52% cumulative accuracy on Lean 4 miniF2F plus five FIMO proofs.",
    "zh-CN": "DeepSeek-Prover 在包含约 800 万条带证明形式命题的合成语料上训练，并报告 Lean 4 miniF2F 累计 52% 的证明率以及 5 道 FIMO 证明。"
  },
  "summary": {
    "en": "Natural-language competition problems are translated into Lean statements, filtered, proved and recycled as synthetic training data.",
    "zh-CN": "自然语言竞赛题被翻译为 Lean 陈述、过滤、搜索证明，再作为合成训练数据回流。"
  },
  "why_it_matters": {
    "en": "The work established synthetic formal-data generation as a scalable route to stronger neural theorem provers.",
    "zh-CN": "该工作确立了“合成形式数据生成”作为扩展神经定理证明器的重要路线。"
  },
  "ai_role": {
    "level": "ai_primary_human_verified",
    "description": {
      "en": "Models generate formal statements and whole proofs that Lean checks mechanically.",
      "zh-CN": "模型生成形式命题与整段证明，由 Lean 进行机械检查。"
    }
  },
  "human_contribution": {
    "description": {
      "en": "Researchers designed the translation, filtering, proof-generation and training pipeline.",
      "zh-CN": "研究者设计翻译、过滤、证明生成与训练管线。"
    }
  },
  "systems": [
    "deepseek-prover",
    "lean"
  ],
  "organizations": [
    "deepseek"
  ],
  "people": [],
  "problems": [
    "minif2f",
    "fimo"
  ],
  "methods": [
    "synthetic-data",
    "whole-proof-generation",
    "lean"
  ],
  "verification": {
    "evidence_level": "E2",
    "status": "paper_released",
    "formal_assurance": "machine_checked",
    "history": [
      {
        "date": "2024-05-23",
        "status": "paper_released",
        "evidence_level": "E2",
        "note": {
          "en": "The Chronicle records this event from the cited primary or official source; the status does not imply independent Chronicle verification.",
          "zh-CN": "Chronicle 依据所引 primary / official source 记录该事件；该状态不表示 Chronicle 已完成独立验证。"
        }
      }
    ]
  },
  "sources": [
    {
      "id": "arxiv-deepseek-prover-2405.14333",
      "tier": "S1",
      "type": "paper",
      "title": "DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data",
      "url": "https://arxiv.org/abs/2405.14333",
      "primary": true,
      "published_date": "2024-05-23",
      "accessed_at": "2026-09-10"
    }
  ],
  "artifacts": [],
  "relationships": {
    "predecessors": [
      "evt-2024-deepseekmath",
      "evt-2021-minif2f"
    ],
    "successors": [],
    "related": []
  },
  "timeline": {
    "featured": true,
    "significance": "primary"
  },
  "tags": [
    "lean",
    "synthetic-data",
    "formal-proof",
    "deepseek"
  ],
  "editorial": {
    "created_at": "2026-09-10",
    "last_reviewed": "2026-09-11",
    "reviewers": [
      "Charlie-Wang-03"
    ]
  },
  "corrections": []
}