Such models can then be used as a scoring function or a component of it. It has been shown, however, that guiding generation by the output of a machine learning model is not a good strategy to generate meaningful samples . Samples generated in such a way have been shown to exploit features unique to the exact models they were optimized for . This is problematic as the generated samples should exhibit the true desired characteristics and not artifacts of a learned model. We call these effects model specific biases.