Text Generation with Efficient (Soft) Q-Learning

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Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2021, background): “[36] use reinforcement learning [132] to generate prompts to control the text generation process.”
Quark: Controllable Text Generation with Reinforced Unlearning (2022, methodology): “In the domain of open-text generation, REINFORCE [75] and PPO [2] have been used for controllable story generation, and soft Q-Learning [20] has been applied to generate prompts for steering language model generations.”
Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering (2022, methodology): “…et al., 2021), summarization (Stiennon et al., 2020; Paulus et al., 2018), machine translation (Shen et al., 2016; Wu et al., 2016), grounded text generation (Ammanabrolu et al., 2021, 2022), controlled text generation (Lu et al., 2022), and prompt generation (Guo et al., 2021; Deng et al., 2022).”
LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks (2022, methodology): “[146] Han Guo, Bowen Tan, Zhengzhong Liu, Eric P Xing, and Zhiting Hu.”
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning (2022, methodology): “We refer interested readers to Guo et al. (2021) for more details.”