> We train each GQN model simultaneously on 4 NVidia K80 GPUs for 2 million gradient steps. The values of the hyper-parameters used for optimisation are detailed in Table S1, and we show the effect of model size on final performance in Fig. S4.

> The values of all hyper-parameters were selected by performing informal search. We did not perform a systematic grid search owing to the high computational cost.

That's nowhere near an obscene amount of computing power for any serious ML project.

Wow. Okay I retract my snide comment. Thanks for finding that.