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    "summary": "Measured patterns linking model size, training data and computing resources to performance. Each pattern concerns a particular measurement and training setup.",
    "definition": "Researchers train different-sized models and fit equations to the results. In language-model research, a common measure is prediction loss: how poorly the model predicts the next text piece.",
    "placement": "Map context: these studies inform expectations about capability growth and resource use.",
    "distinction": "Larger is not the only choice. Hoffmann and coauthors found better results by balancing model size with more training data. Extrapolating a measured trend to untested scales adds an assumption.",
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        "scaling-chinchilla"
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        "scaling-chinchilla"
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      "id": "scaling-kaplan",
      "title": "Scaling Laws for Neural Language Models",
      "publisher": "Jared Kaplan and coauthors / arXiv",
      "url": "https://arxiv.org/abs/2001.08361",
      "published": "2020-01-23",
      "checkedOn": "2026-09-15",
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      "notes": "Read abstract and submission record. Empirical relationships concern language-model prediction loss and training resources, not an AGI date."
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    {
      "id": "scaling-chinchilla",
      "title": "Training Compute-Optimal Large Language Models",
      "publisher": "Jordan Hoffmann and coauthors / arXiv",
      "url": "https://arxiv.org/abs/2203.15556",
      "published": "2022-03-29",
      "checkedOn": "2026-09-15",
      "kind": "primary",
      "verification": "read",
      "notes": "Read abstract and submission record. Tests balance parameter count and training tokens under a fixed compute budget. Results are scoped to the studied setups."
    }
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}