Meet RLPrompt: A New Prompt Optimization Approach with Reinforcement Learning (RL) - MarkTechPost

Prompting is a promising approach to solving NLP problems with pre-trained language models (LMs) such as GPTs and BERT. Unlike conventional fine-tuning that updates the massive LM parameters for each downstream task, prompting concatenates inputs with additional text to steer the LM towards producing the desired outputs. A key question is finding optimal prompts to improve the LM's performance on various tasks with few training examples. Reinforcement Learning (RL) for prompt optimization challenges learning efficiency as the large black-box language model navigates a complex environment involving multiple transitions before computing rewards. This complexity makes it challenging to learn from the unstable reward signals.