  Maximum likelihood estimation (MLE) is the predominant algorithm for training
text generation models. This paradigm relies on direct supervision examples,
which is not applicable to many emerging applications, such as generating
adversarial attacks or generating prompts to control language models.
Reinforcement learning (RL) on the other hand offers a more flexible solution
by allowing users to plug in arbitrary task metrics as reward. Yet previous RL
algorithms for text generation, such as policy gradient (on-policy RL) and
Q-learning (off-policy RL), are often notoriously inefficient or unstable to
train due to the large sequence space and the sparse reward received only at
the end of sequences. In this paper, we introduce a new RL formulation for text
generation from the soft Q-learning (SQL) perspective. It enables us to draw
from the latest RL advances, such as path consistency learning, to combine the
best of on-/off-policy updates, and learn effectively from sparse reward. We
apply the approach to a wide range of novel text generation tasks, including
learning from noisy/negative examples, adversarial attacks, and prompt
generation. Experiments show our approach consistently outperforms both
task-specialized algorithms and the previous RL methods.
