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Let me try to explain here. Usually, we calculate the variance by subtracting the mean term and then square it. But here mean (first-moment m_t) is fluctuating like anything at each time "t" and is getting calculated with the influence of past mean as well, also with the influence of beta_1. So when the 2nd-moment term v_t is getting calculated ...


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There is an existence of a loss/reward function for any task that can be evaluated, but this does not mean that function generalizes to any model. In your prompt you mention "human-level performance", this assumes a metric such as accuracy, auc, precision, winning %, etc that humans were evaluated on for some task. Since it is possible to model any ...


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