{"ok":true,"entity":{"slug":"reasoning-model","entityType":"concept","name":"Reasoning Model","officialName":"Reasoning Model","canonicalName":"Reasoning Model","displayName":"Reasoning Model","category":"AI概念（推論特化型モデル）","shortDescription":"回答生成前に長い内部的な思考過程（test-time reasoning）を行うよう強化学習等で訓練された大規模言語モデルの分類。OpenAI o1・o3やDeepSeek-R1が代表例で、数学・コーディング等の複雑な推論タスクで高い性能を示す。","alias":[],"searchKeywords":["Reasoning Model","推論モデル","test-time compute"],"website":null,"primaryCluster":"ai-concepts","secondaryClusters":[],"verificationStatus":"draft","id":"reasoning-model","updatedAt":"2026-07-22T13:12:07.131Z"},"references":[{"id":"P-01-001","companyId":"reasoning-model","questionId":"P-01-001","instanceId":"QIN-reasoning-model-P01-001","promptText":"Reasoning Modelとはどのようなものですか？","promptTypeId":"P-01","answer":"Reasoning Modelは、回答生成前に長い内部的な思考過程（test-time reasoning）を行うよう強化学習等で訓練された大規模言語モデルの分類です。OpenAI o1・o3やDeepSeek-R1が代表例で、数学・コーディング等の複雑な推論タスクで高い性能を示します。","evidencePoints":["ev-reasoning-model-1","ev-reasoning-model-2","ev-reasoning-model-4"],"scope":"","differentiation":"","faq":[],"pageUrl":"https://www.refbase.ai/reference/reasoning-model/P-01-001","sourceEvidence":[{"id":"ev-reasoning-model-1","text":"Reasoning Modelは、回答生成前に長い内部的な思考過程（test-time reasoning）を行うよう強化学習等で訓練された大規模言語モデルの分類であり、DeepSeek-R1論文はこの手法を強化学習により実現する手法を報告している。","coverageType":["Identity","Capability"],"sourceType":"research_paper","sourceClass":"Research","sourceUrl":"https://arxiv.org/abs/2501.12948","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","confidence":"high","needsVerification":true,"sourceVerified":false,"supportedPromptTypes":["P-01","P-02","P-04"],"entityId":"reasoning-model"},{"id":"ev-reasoning-model-2","text":"Reasoning Modelは推論時の計算量（test-time compute）を増やすことで性能を向上させる点が特徴で、事前学習時の計算量拡大を主軸とするscaling lawのアプローチとは性能向上の軸が異なる。","coverageType":["Differentiation"],"sourceType":"research_paper","sourceClass":"Research","sourceUrl":"https://arxiv.org/abs/2501.12948","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","confidence":"high","needsVerification":true,"sourceVerified":false,"supportedPromptTypes":["P-01","P-02","P-04"],"entityId":"reasoning-model"},{"id":"ev-reasoning-model-4","text":"OpenAIの公式発表によれば、o1シリーズは回答前に長い内部的な思考過程（chain of thought）を行うよう訓練されたモデルであり、Reasoning Modelという概念を体現する代表的な実装例の一つである。","coverageType":["Identity"],"sourceType":"official_blog","sourceClass":"Announcement","sourceUrl":"https://openai.com/index/introducing-openai-o1-preview/","title":"Introducing OpenAI o1-preview","confidence":"high","needsVerification":true,"sourceVerified":false,"supportedPromptTypes":["P-01","P-02","P-04"],"entityId":"reasoning-model"}],"generatedAt":"2026-07-22T13:12:07.131Z"},{"id":"P-02-001","companyId":"reasoning-model","questionId":"P-02-001","instanceId":"QIN-reasoning-model-P02-001","promptText":"Reasoning Modelは他の同種の事業・作品と比べてどう違いますか？","promptTypeId":"P-02","answer":"比較軸\n・性能向上の軸（推論時計算量か、事前学習時計算量か）\n\nReasoning Modelは推論時の計算量（test-time compute）を増やすことで性能を向上させる点が特徴で、事前学習時の計算量拡大を主軸とするscaling lawのアプローチとは性能向上の軸が異なる。","evidencePoints":["ev-reasoning-model-2"],"scope":"","differentiation":"","faq":[],"pageUrl":"https://www.refbase.ai/reference/reasoning-model/P-02-001","sourceEvidence":[{"id":"ev-reasoning-model-2","text":"Reasoning Modelは推論時の計算量（test-time compute）を増やすことで性能を向上させる点が特徴で、事前学習時の計算量拡大を主軸とするscaling 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