The paper "MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization" addresses the challenge of optimizing prompts for Large Language Models (LLMs) to enhance their performance across various downstream tasks in Natural Language Processing (NLP). The authors demonstrate that different prompts should be adapted to different LLMs to improve their capabilities. They propose a novel method called Model-Adaptive Prompt Optimizer (MAPO), which optimizes prompts for each specific LLM in downstream tasks using a combination of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). The method involves generating candidate prompts, establishing a warm-up dataset, and refining these prompts through RL. Extensive experiments on three LLMs (BLOOM-7B, GPT-J-6B, and LLaMA-7B) across question-answering, classification, and generation tasks show that MAPO significantly improves performance compared to SFT alone. The paper also includes ablation studies, error analysis, and exploratory analysis to validate the effectiveness and limitations of the proposed method.The paper "MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization" addresses the challenge of optimizing prompts for Large Language Models (LLMs) to enhance their performance across various downstream tasks in Natural Language Processing (NLP). The authors demonstrate that different prompts should be adapted to different LLMs to improve their capabilities. They propose a novel method called Model-Adaptive Prompt Optimizer (MAPO), which optimizes prompts for each specific LLM in downstream tasks using a combination of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). The method involves generating candidate prompts, establishing a warm-up dataset, and refining these prompts through RL. Extensive experiments on three LLMs (BLOOM-7B, GPT-J-6B, and LLaMA-7B) across question-answering, classification, and generation tasks show that MAPO significantly improves performance compared to SFT alone. The paper also includes ablation studies, error analysis, and exploratory analysis to validate the effectiveness and limitations of the proposed method.