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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

Scaling Scaling Laws with Board Games

Andy L. Jones This is my paper
classification cs.LG cs.MA
keywords experimentscomputeperformanceresultsscalingsequencesizeachievable
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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 6 Pith papers

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