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OpenAI finds predictable scaling laws that guide GPT-3 training

OpenAI reported power-law relationships between language-model loss, size, data, and compute. Its separately dated GPT-3 paper later stated that these laws directly guided model-size, data, and training-compute decisions for the 175-billion-parameter system.

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CURRENT ASSESSMENT · REVISION 1
TOWARD DOOM61confidence 96/100

Why it moved the index

Later primary GPT-3 evidence confirms that the scaling laws guided concrete frontier-training decisions, directly accelerating broadly transferable capability and synthetic-text manipulation potential.

Assessment history

  1. R1
    Toward 61 · confidence 96

    New historical research milestone with separately dated primary evidence of practical downstream use in GPT-3 development.

    11 Aug 2026

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  1. DoomBench assesses “OpenAI finds predictable scaling laws that guide GPT-3 training” as evidence moving toward doom, with magnitude 61 and confidence 96 out of 100 in the capability gains category.

  2. The DoomBench assessment of “OpenAI finds predictable scaling laws that guide GPT-3 training” is based on reporting from OpenAI and records the editorial rationale, source quality, attribution, and revision history.

  3. DoomBench summarizes “OpenAI finds predictable scaling laws that guide GPT-3 training” as follows: OpenAI reported power-law relationships between language-model loss, size, data, and compute. Its separately dated GPT-3 paper later...

    https://www.doombench.com/news/openai-finds-predictable-scaling-laws-that-guide-gpt-3-training-2020-01-23