  We give a near-optimal sample-pass trade-off for pure exploration in
multi-armed bandits (MABs) via multi-pass streaming algorithms: any streaming
algorithm with sublinear memory that uses the optimal sample complexity of
$O(\frac{n}{\Delta^2})$ requires
$\Omega(\frac{\log{(1/\Delta)}}{\log\log{(1/\Delta)}})$ passes. Here, $n$ is
the number of arms and $\Delta$ is the reward gap between the best and the
second-best arms. Our result matches the $O(\log(\frac{1}{\Delta}))$-pass
algorithm of Jin et al. [ICML'21] (up to lower order terms) that only uses
$O(1)$ memory and answers an open question posed by Assadi and Wang [STOC'20].
