Prompts from Reinforcement Learning (PRL)

This paper introduces PRL (Prompts from Reinforcement Learning), a novel method that automatically generates and refines prompts for Large Language Models (LLMs) using reinforcement learning. Unlike previous methods, PRL can create new, task-specific few-shot examples that were not part of the training data, leading to state-of-the-art performance across various natural language processing tasks, including classification, summarization, and simplification. The approach incorporates a reasoning phase before prompt generation and a prompt selection strategy to improve robustness and efficiency, demonstrating that even larger LLMs benefit from these optimized prompts and that effective prompting is task-dependent.