Title: PRL: Prompts from Reinforcement Learning
Primary area: foundation or frontier models, including LLMs
Keywords: Large Language Models, Prompt Engineering, Reasoning, Automatic Prompt Generation

Abstract: Effective prompt engineering remains a central challenge in fully harnessing the
capabilities of LLMs. While well-designed prompts can dramatically enhance
performance, crafting them typically demands expert intuition and a nuanced understanding of the task. Moreover, the most impactful prompts often hinge on
subtle semantic cues, ones that may elude human perception but are crucial for
guiding LLM behavior. In this paper, we introduce PRL (Prompts from Reinforcement Learning), a novel RL-based approach for automatic prompt generation.
Unlike previous methods, PRL can produce novel few-shot examples that were not
seen during training. Our approach achieves state-of-the-art performance across a
range of benchmarks, including text classification, simplification, summarization,
and reasoning. On the classification task, it surpasses prior methods by 2.58%
over APE and 1.00% over EvoPrompt. Additionally, it improves the average
ROUGE scores on the summarization task by 4.32 over APE and by 2.12 over
EvoPrompt and the SARI score on simplification by 6.93 over APE and by 6.01
over EvoPrompt. On the GSM8K mathematical reasoning benchmark, PRL further
improves accuracy by 2.72% over APE and by 4.53% over EvoPrompt. We will
make our implementation publicly available upon acceptance.

TL;DR: We propose to learn prompts through reinforcement learning, obtaining prompt including newly synthesized few-shot examples and outperforming other automatic prompting methods on text classification, summarization, simplification and GSM8K