To illustrate this copy problem, we show how a trivial model, we call AddCarbon model, can trick most of the distribution learning metrics in We evaluate this model using the GuacaMol distributionlearning benchmark ). The AddCarbon model obtains near perfect benchmarking scores and outperforms many complex generative models (see Table ). By construction it has a perfect novelty and validity of 100%, while also having an almost perfect uniqueness, and a very high Kullback-Leibler (KL) divergence metric.