If we have a set of models trained independently with different wait-k policies, then we can apply ensemble of those models (Dietterich, 2000; Hansen and Salamon, 1990) to improve the translation quality, which is also used to improve the translation quality of full-sentence translation (Stahlberg and Byrne, 2017).
there may be two issues to apply ensemble of all models: (1) the runtime for each prediction could be longer, resulting in higher latency; and (2) the translation accuracy may be worse, for the best model for one policy may give bad performance when doing inference with another policy.