  We propose a new learning framework that captures the tiered structure of
many real-world user-interaction applications, where the users can be divided
into two groups based on their different tolerance on exploration risks and
should be treated separately. In this setting, we simultaneously maintain two
policies $\pi^{\text{O}}$ and $\pi^{\text{E}}$: $\pi^{\text{O}}$ ("O" for
"online") interacts with more risk-tolerant users from the first tier and
minimizes regret by balancing exploration and exploitation as usual, while
$\pi^{\text{E}}$ ("E" for "exploit") exclusively focuses on exploitation for
risk-averse users from the second tier utilizing the data collected so far. An
important question is whether such a separation yields advantages over the
standard online setting (i.e., $\pi^{\text{E}}=\pi^{\text{O}}$) for the
risk-averse users. We individually consider the gap-independent
vs.~gap-dependent settings. For the former, we prove that the separation is
indeed not beneficial from a minimax perspective. For the latter, we show that
if choosing Pessimistic Value Iteration as the exploitation algorithm to
produce $\pi^{\text{E}}$, we can achieve a constant regret for risk-averse
users independent of the number of episodes $K$, which is in sharp contrast to
the $\Omega(\log K)$ regret for any online RL algorithms in the same setting,
while the regret of $\pi^{\text{O}}$ (almost) maintains its online regret
optimality and does not need to compromise for the success of $\pi^{\text{E}}$.
