@inproceedings{jiang-etal-2025-s3,
    title = "s3: You Don{'}t Need That Much Data to Train a Search Agent via {RL}",
    author = "Jiang, Pengcheng  and
      Xu, Xueqiang  and
      Lin, Jiacheng  and
      Xiao, Jinfeng  and
      Wang, Zifeng  and
      Sun, Jimeng  and
      Han, Jiawei",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1095/",
    pages = "21610--21628",
    ISBN = "979-8-89176-332-6",
    abstract = {Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search agents via reinforcement learning (RL), improving information acquisition through multi-turn interactions with retrieval engines. However, existing approaches either optimize retrieval using search-only metrics (e.g., NDCG) that ignore downstream utility or fine-tune the entire LLM to jointly reason and retrieve{---}entangling retrieval with generation and limiting the real search utility and compatibility with frozen or proprietary models. In this work, we propose **s3**, a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the searcher using a Gain Beyond RAG reward: the improvement in generation accuracy over na{\"i}ve RAG. **s3** requires only 2.4k training samples to outperform baselines trained on over 70 $\times$ more data, consistently delivering stronger downstream performance across six general QA and five medical QA benchmarks.}
}