{
  "schema_version": 1,
  "id": "evt-2024-openai-o1",
  "slug": "openai-o1-reasoning-aime",
  "title": {
    "en": "OpenAI o1 introduces inference-time reasoning with a large AIME performance jump",
    "zh-CN": "OpenAI o1 以推理时思考带来 AIME 能力跃升"
  },
  "dates": {
    "event": "2024-09-12",
    "first_public_claim": "2024-09-12",
    "paper_release": null,
    "artifact_release": null,
    "independently_verified": null,
    "added_to_chronicle": "2026-09-10",
    "last_updated": "2026-09-10"
  },
  "event_types": [
    "system",
    "benchmark"
  ],
  "significance": {
    "tier": "H3",
    "rationale": {
      "en": "OpenAI o1 was a major general-purpose reasoning-model release, but its place in the AI4Math Chronicle is contextual: AIME demonstrated capability rather than the release itself contributing a new mathematical result, proof, formalization, or math-specific system.",
      "zh-CN": "OpenAI o1 是重要的通用推理模型发布，但在 AI4Math Chronicle 中主要承担背景作用：AIME 用于展示模型能力，而该发布本身并未贡献新的数学结果、证明、形式化或数学专项系统。"
    }
  },
  "mathematical_novelty": {
    "type": "benchmark_result"
  },
  "interfaces": [
    "informal",
    "symbolic"
  ],
  "claim": {
    "en": "OpenAI reported o1 at 74% single-sample accuracy on AIME 2024 and 93% after reranking 1,000 samples, far above GPT-4o's 12% baseline.",
    "zh-CN": "OpenAI 报告 o1 在 AIME 2024 上单样本准确率为 74%，对 1,000 个样本重排序后达到 93%，显著高于 GPT-4o 的 12%。"
  },
  "summary": {
    "en": "The o1 series was trained to spend more inference-time computation on multi-step reasoning before producing an answer.",
    "zh-CN": "o1 系列被训练为在给出答案前投入更多推理时计算执行多步思考。"
  },
  "why_it_matters": {
    "en": "The release helped establish test-time compute and long reasoning traces as central levers for frontier mathematical performance, providing context for later math-specific systems.",
    "zh-CN": "该发布推动“测试时计算”和长程推理轨迹成为提升前沿数学能力的重要杠杆，为后续数学专项系统提供背景。"
  },
  "ai_role": {
    "level": "computation_only",
    "description": {
      "en": "The model performs extended internal reasoning before answering mathematical problems.",
      "zh-CN": "模型在回答数学问题前执行更长的内部推理。"
    }
  },
  "human_contribution": {
    "description": {
      "en": "OpenAI researchers designed the reasoning training method and benchmark evaluations.",
      "zh-CN": "OpenAI 研究者设计推理训练方法与基准评测。"
    }
  },
  "systems": [
    "openai-o1"
  ],
  "organizations": [
    "openai"
  ],
  "people": [],
  "problems": [
    "aime-2024"
  ],
  "methods": [
    "reasoning-model",
    "test-time-compute",
    "reinforcement-learning"
  ],
  "verification": {
    "evidence_level": "E1",
    "status": "claimed",
    "formal_assurance": "none",
    "history": [
      {
        "date": "2024-09-12",
        "status": "claimed",
        "evidence_level": "E1",
        "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": "openai-o1-reasoning-2024",
      "tier": "S2",
      "type": "official_announcement",
      "title": "Learning to reason with LLMs",
      "url": "https://openai.com/index/learning-to-reason-with-llms/",
      "primary": true,
      "published_date": "2024-09-12",
      "accessed_at": "2026-09-10"
    }
  ],
  "artifacts": [],
  "relationships": {
    "predecessors": [
      "evt-2022-minerva",
      "evt-2021-math-benchmark"
    ],
    "successors": [],
    "related": []
  },
  "timeline": {
    "featured": false,
    "significance": "context"
  },
  "tags": [
    "openai",
    "reasoning-model",
    "aime",
    "test-time-compute"
  ],
  "editorial": {
    "created_at": "2026-09-10",
    "last_reviewed": "2026-09-11",
    "reviewers": [
      "Charlie-Wang-03"
    ]
  },
  "corrections": []
}