  Dynamic pricing models often posit that a $\textbf{stream}$ of customer
interactions occur sequentially, where customers' valuations are drawn
independently. However, this model is not entirely reflective of the real
world, as it overlooks a critical aspect, the law of diminishing marginal
utility, which states that a customer's marginal utility from each additional
unit declines. This causes the valuation distribution to shift towards the
lower end, which is not captured by the stream model. This motivates us to
study a pool-based model, where a $\textbf{pool}$ of customers repeatedly
interacts with a monopolist seller, each of whose valuation diminishes in the
number of purchases made according to a discount function. In particular, when
the discount function is constant, our pool model recovers the stream model. We
focus on the most fundamental special case, where a customer's valuation
becomes zero once a purchase is made. Given $k$ prices, we present a
non-adaptive, detail-free (i.e., does not "know" the valuations) policy that
achieves a $1/k$ competitive ratio, which is optimal among non-adaptive
policies. Furthermore, based on a novel debiasing technique, we propose an
adaptive learn-then-earn policy with a $\tilde O(k^{2/3} n^{2/3})$ regret.
