@inproceedings{liu-etal-2026-prompt,
    title = "Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning",
    author = "Liu, Wenjin  and
      Luo, Haoran  and
      Lin, Xueyuan  and
      Liu, Haoming  and
      Shen, Tiesunlong  and
      Wang, Jiapu  and
      Mao, Rui  and
      Cambria, Erik",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.799/",
    pages = "16260--16280",
    ISBN = "979-8-89176-395-1",
    abstract = "Recently, various excellent and powerful large language models (LLMs) have been utilized to solve a wide range of human problems. However, when faced with complex problems, most users are often unable to provide accurate and effective prompts to interact with LLMs, thus limiting their performance. To address this challenge, we propose Prompt-R1, an end-to-end reinforcement learning framework that utilizes a small-scale LLM (as \textit{agent}) to collaborate with large-scale LLMs (as \textit{environment}), replacing users to interact better. This collaboration is presented as a multi-turn interaction, where the small-scale LLM thinks and generates prompts, and the large-scale LLM performs complex reasoning. A double-constrained reward is designed to optimize correctness and quality of generation. Prompt-R1 provides a plug-and-play framework that supports both inference and training with various large-scale LLMs. Experimental results on twelve datasets show that Prompt-R1 significantly outperforms baseline LLMs across various tasks.Our code is available at https://github.com/QwenQKing/Prompt-R1."
}