  Previous work has shown that artificial neural agents naturally develop
surprisingly non-efficient codes. This is illustrated by the fact that in a
referential game involving a speaker and a listener neural networks optimizing
accurate transmission over a discrete channel, the emergent messages fail to
achieve an optimal length. Furthermore, frequent messages tend to be longer
than infrequent ones, a pattern contrary to the Zipf Law of Abbreviation (ZLA)
observed in all natural languages. Here, we show that near-optimal and
ZLA-compatible messages can emerge, but only if both the speaker and the
listener are modified. We hence introduce a new communication system,
"LazImpa", where the speaker is made increasingly lazy, i.e. avoids long
messages, and the listener impatient, i.e.,~seeks to guess the intended content
as soon as possible.
