Domain-Aware Reinforcement Learning for Prompt Optimization
Year: 2025
Type: article
Abstract: Prompt engineering provides an efficient way to adapt large language models (LLMs) to downstream tasks without retraining model parameters. However, designing effective prompts can be challenging, especially when model gradients are unavailable and human expertise is required. Existing automated methods based on gradient optimization or heuristic search exhibit inherent limitations under black box or limited-query conditions. We propose Domain-Aware Reinforcement Learning for Prompt Optimization (DA-RLPO), which treats prompt editing as a sequential decision process and leverages structured domain knowledge to constrain candidate edits. Our experimental results show that DA-RLPO achieves higher accuracy than baselines on text classification tasks and maintains robust performance with limited API calls, while also demonstrating effectiveness on text-to-image and reasoning tasks.
Source: Mathematics
DOI: 10.3390/math13162552
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4413223923