s3: You Don't Need That Much Data to Train a Search Agent via RL
Authors: name: Pengcheng Jiang, name: Xueqiang Xu, name: Jiacheng Lin, name: Jinfeng Xiao, name: Zifeng Wang, name: Jimeng Sun, name: Jiawei Han

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 naive RAG.
s3 requires only 2.4k training samples to outperform baselines trained on over
70x more data, consistently delivering stronger downstream performance across
six general QA and five medical QA benchmarks.