You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Reinforcement Learning for Task-Oriented Dialogue Systems. Applying the standard RL meth- ods straightforwardly to optimize a task-oriented dialogue agent causes the issue of diverging from human language. To address this problem, interleaving reinforcement learning with supervised learning has been proposed but it is still not free from the issue of diverging from human language (Lewis et al., 2017). Recently, the latent representation models for language actions have been in- troduced to address the aforementioned problem (Zhao et al., 2019; Yarats & Lewis, 2018). They disentangle the semantics of the utterance and the natural language generation, and then perform goal-based training in the space of the latent variables instead of directly optimizing utterances. However, they cannot be directly applied to large-scale pre-trained language models that are not designed in a way that works inherently with discrete latent variables. Jaques et al. (2020) use KL- control to restrict the policy to stay close to its prior policy, but it still suffers from divergence from human language even with carefully chosen hyper-parameters. Furthermore, Jang et al. (2020) adopt Bayes-adaptive Monte-Carlo planning to negotiation dialogue then use it as a policy improvement operator. This approach can prevent the issue of diverging from human language through the policy improvement based on behavior cloning of self-generated dialogues. However, they assume a user model that is difﬁcult enough to be considered another problem.

Question: What does “KL control” means?
KL control means that the regularization technique to restrict the policy to stay close to its prior policy.