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This paper proposes a general method for improving the structure and qualityof sequences generated by a recurrent neural network (RNN), while maintaininginformation originally learned from data, as well as sample diversity. An RNNis first pre-trained on data using maximum likelihood estimation (MLE), and theprobability distribution over the next token in the sequence learned by thismodel is treated as a prior policy. Another RNN is then trained usingreinforcement learning (RL) to generate higher-quality outputs that account fordomain-specific incentives while retaining proximity to the prior policy of theMLE RNN. To formalize this objective, we derive novel off-policy RL methods forRNNs from KL-control. The effectiveness of the approach is demonstrated on twoapplications; 1) generating novel musical melodies, and 2) computationalmolecular generation. For both problems, we show that the proposed methodimproves the desired properties and structure of the generated sequences, whilemaintaining information learned from data.

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control