  Instant Search is a paradigm where a search system retrieves answers on the
fly while typing. The na\"ive implementation of an Instant Search system would
hit the search back-end for results each time a user types a key, imposing a
very high load on the underlying search system. In this paper, we propose to
address the load issue by identifying tokens that are semantically more salient
towards retrieving relevant documents and utilize this knowledge to trigger an
instant search selectively. We train a reinforcement agent that interacts
directly with the search engine and learns to predict the word's importance.
Our proposed method treats the underlying search system as a black box and is
more universally applicable to a diverse set of architectures. Furthermore, a
novel evaluation framework is presented to study the trade-off between the
number of triggered searches and the system's performance. We utilize the
framework to evaluate and compare the proposed reinforcement method with other
intuitive baselines. Experimental results demonstrate the efficacy of the
proposed method towards achieving a superior trade-off.
