  Starting from Robbins (1952), the literature on experimentation via
multi-armed bandits has wed exploration and exploitation. Nonetheless, in many
applications, agents' exploration and exploitation need not be intertwined: a
policymaker may assess new policies different than the status quo; an investor
may evaluate projects outside her portfolio. We characterize the optimal
experimentation policy when exploration and exploitation are disentangled in
the case of Poisson bandits, allowing for general news structures. The optimal
policy features complete learning asymptotically, exhibits lots of persistence,
but cannot be identified by an index a la Gittins. Disentanglement is
particularly valuable for intermediate parameter values.
