Unfortunately, executing LOLS naively in our setting is prohibitive because it would run inference from scratch millions of times under different policies, training examples, and variations of the decision sequence.
this paper presents efficient algorithms for repeated inference, which are applicable to a wide variety of NLP tasks, including parsing, machine translation and sequence tagging.