  Prompting has shown impressive success in enabling large pretrained language
models (LMs) to perform diverse NLP tasks, especially when only few downstream
data are available. Automatically finding the optimal prompt for each task,
however, is challenging. Most existing work resorts to tuning soft prompt
(e.g., embeddings) which falls short of interpretability, reusability across
LMs, and applicability when gradients are not accessible. Discrete prompt, on
the other hand, is difficult to optimize, and is often created by "enumeration
(e.g., paraphrasing)-then-selection" heuristics that do not explore the prompt
space systematically. This paper proposes RLPrompt, an efficient discrete
prompt optimization approach with reinforcement learning (RL). RLPrompt
formulates a parameter-efficient policy network that generates the desired
discrete prompt after training with reward. To overcome the complexity and
stochasticity of reward signals by the large LM environment, we incorporate
effective reward stabilization that substantially enhances the training
efficiency. RLPrompt is flexibly applicable to different types of LMs, such as
masked (e.g., BERT) and left-to-right models (e.g., GPTs), for both
classification and generation tasks. Experiments on few-shot classification and
unsupervised text style transfer show superior performance over a wide range of
existing finetuning or prompting methods. Interestingly, the resulting
optimized prompts are often ungrammatical gibberish text; and surprisingly,
those gibberish prompts are transferrable between different LMs to retain
significant performance, indicating LM prompting may not follow human language
patterns.
