  We present evidence of substantial benefit from efficient exploration in
gathering human feedback to improve large language models. In our experiments,
an agent sequentially generates queries while fitting a reward model to the
feedback received. Our best-performing agent generates queries using double
Thompson sampling, with uncertainty represented by an epistemic neural network.
Our results demonstrate that efficient exploration enables high levels of
performance with far fewer queries. Further, both uncertainty estimation and
the choice of exploration scheme play critical roles.
