  Likelihood training and maximization-based decoding result in dull and
repetitive generated texts even when using powerful language models (Holtzman
et al., 2019). Adding a loss function for regularization was shown to improve
text generation output by helping avoid unwanted properties, such as
contradiction or repetition (Li at al., 2020). In this work, we propose
fine-tuning a language model by using policy gradient reinforcement learning,
directly optimizing for better generation. We apply this approach to minimizing
repetition in generated text, and show that, when combined with unlikelihood
training (Welleck et al., 2020), our method further reduces repetition without
impacting the language model quality. We also evaluate other methods for
improving generation at training and decoding time, and compare them using
various metrics aimed at control for better text generation output.
