{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/test-time-compute-scaling",
  "headline": "Test-Time Compute Scaling: Inference-Time Reasoning Paradigms from o1/o3 to Forest-of-Thought",
  "description": "Test-Time Compute Scaling represents a paradigm shift: instead of making models bigger during training, allocate more computation during inference for deeper reasoning. OpenAI o1/o3 demonstrated that \"thinking longer\" enables PhD-level scientific reasoning and competitive programming — reshaping the scaling landscape from pre-training to inference.",
  "dateCreated": "2026-05-24T02:49:13.665Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "OpenAI o1 System Card: Learning to Reason with Large Language Models",
      "sameAs": "https://openai.com/index/learning-to-reason-with-llms/"
    },
    {
      "@type": "CreativeWork",
      "name": "Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning",
      "sameAs": "https://arxiv.org/abs/2412.09078"
    }
  ]
}