Existing prompt optimization methods often improve prompts for a specific task or deployment setting, but their optimized prompts transfer poorly to new tasks and usually require re-optimization. We propose Prompting to Prompt (PTP), a framework that learns meta-templates as structured intermediate representations for reusable prompt construction. PTP decomposes prompts into transferable elements and optimizes them with LLM-generated textual gradients. Its inner loop refines task-specific prompts from natural-language feedback and an element list, while its outer loop abstracts structural changes across tasks to update the meta-template.The learned meta-template is then reused for task-level and query-level prompt generation, reducing the need to rerun full prompt optimization for each target setting. Experiments on six benchmarks show gains in both offline transfer and test-time deployment settings, with improvements up to 10.52 points on Arena-Hard.

In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs), and recent studies introduce CoT to exemplars of ICL to enhance the reasoning capability, especially in mathematics tasks. However, given the continuous advancement of model capabilities, it remains unclear whether CoT exemplars still benefit recent, stronger models in such tasks. Through systematic experiments, we find that for recent strong models such as the Qwen2.5 series, adding traditional CoT exemplars does not improve reasoning performance compared to Zero-Shot CoT. Instead, their primary function is to align the output format with human expectations. We further investigate the effectiveness of enhanced CoT exemplars, constructed using answers from advanced models such as Qwen2.5-Max and DeepSeek-R1. Experimental results indicate that these enhanced exemplars still fail to improve the model’s reasoning performance. Further analysis reveals that models tend to ignore the exemplars and focus primarily on the instructions, leading to no observable gain in reasoning ability. Overall, our findings highlight the limitations of the current ICL+CoT framework in mathematical reasoning, calling for a re-examination of the ICL paradigm and the definition of exemplars.