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This "early velocity only" approach seems like a problem - how do you know with 5-minute training runs that you aren't affecting the overall asymptote? e.g., what if the AI picks a quantizer that happens to be faster in the first five minutes, but has a big noise floor where it can't make more progress?

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Yes, it's greedy so may hit local optima. You can fit learning curves and try to extrapolate out to avoid that problem, to let you run long enough to be reasonably sure of a dead end, and periodically revive past candidates to run longer. See past hyperparameter approaches like freeze-thaw https://arxiv.org/abs/1406.3896 .

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