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Scaling Scaling Laws with Board Games

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arxiv 2104.03113 v2 pith:K7WWZAJL submitted 2021-04-07 cs.LG cs.MA

Andy L. Jones This is my paper · ORCID
classification cs.LGcs.MA
keywords experimentscomputeperformanceresultsscalingsequencesizeachievable
verification ladder T0 review T1 audit T2 compute T3 formal
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The largest experiments in machine learning now require resources far beyond the budget of all but a few institutions. Fortunately, it has recently been shown that the results of these huge experiments can often be extrapolated from the results of a sequence of far smaller, cheaper experiments. In this work, we show that not only can the extrapolation be done based on the size of the model, but on the size of the problem as well. By conducting a sequence of experiments using AlphaZero and Hex, we show that the performance achievable with a fixed amount of compute degrades predictably as the game gets larger and harder. Along with our main result, we further show that the test-time and train-time compute available to an agent can be traded off while maintaining performance.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 8 citations worldwide. Full citation record

  1. A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search

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    A skill-graph random-walk model gives closed-form accuracy-versus-compute formulas for four reasoning strategies and connects them to training scaling.

  2. ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models

    cs.CV 2025-02 conditional novelty 6.0 of 10

    ZeroBench is a hand-built 100-question visual reasoning benchmark, adversarially filtered so every evaluated frontier LMM scored 0% at release.

  3. Inference Scaling Reshapes AI Governance

    cs.CY 2025-02 conditional novelty 6.0 of 10

    If frontier AI progress shifts from pre-training compute to inference-time compute, AI governance must be rebuilt around deployment-time capabilities and transparency, with different implications depending on whether ...

  4. Meek Models Shall Inherit the Earth

    cs.AI 2025-07 conditional novelty 5.0 of 10

    Under fixed-distribution neural scaling laws, the capability gap between state-of-the-art and low-compute AI models shrinks over time toward zero.

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