System Prompt Learning: Teaching LLMs to Learn Problem-Solving Strategies from Experience

System Prompt Learning (SPL) is a new paradigm designed to enhance the problem-solving capabilities of Large Language Models (LLMs) by allowing them to learn from experience. Implemented as an open-source plugin in optillm, SPL bridges the gap found in basic system prompts by integrating explicit problem-solving strategies, thus improving performance across various benchmarks. It classifies problems into specific types, applies relevant strategies, and refines them over time based on success rates and new examples, while maintaining human-readable transparency. The approach has shown significant improvements, particularly in challenging benchmarks like Arena Auto Hard and AIME24, by enabling models to develop a dynamic database of strategies that evolve, improving efficiency and adaptability. This method not only allows LLMs to reuse successful approaches and adapt to different problem types but also opens possibilities for domain-specific expertise, collaborative learning, and human-AI collaboration.