WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents
Authors: name: Junteng Liu; name: Yunji Li; name: Chi Zhang; name: Jingyang Li; name: Aili Chen; name: Ke Ji; name: Weiyu Cheng; name: Zijia Wu; name: Chengyu Du; name: Qidi Xu; name: Jiayuan Song; name: Zhengmao Zhu; name: Wenhu Chen; name: Pengyu Zhao; name: Junxian He
Published: 2025-09-08 10:07:03

Abstract: The paradigm of Large Language Models (LLMs) has increasingly shifted toward
agentic applications, where web browsing capabilities are fundamental for
retrieving information from diverse online sources. However, existing
open-source web agents either demonstrate limited information-seeking abilities
on complex tasks or lack transparent implementations. In this work, we identify
that the key challenge lies in the scarcity of challenging data for information
seeking. To address this limitation, we introduce WebExplorer: a systematic
data generation approach using model-based exploration and iterative,
long-to-short query evolution. This method creates challenging query-answer
pairs that require multi-step reasoning and complex web navigation. By
leveraging our curated high-quality dataset, we successfully develop advanced
web agent WebExplorer-8B through supervised fine-tuning followed by
reinforcement learning. Our model supports 128K context length and up to 100
tool calling turns, enabling long-horizon problem solving. Across diverse
information-seeking benchmarks, WebExplorer-8B achieves the state-of-the-art
performance at its scale. Notably, as an 8B-sized model, WebExplorer-8B is able
to effectively search over an average of 16 turns after RL training, achieving
higher accuracy than WebSailor-72B on BrowseComp-en/zh and attaining the best
performance among models up to 100B parameters on WebWalkerQA and FRAMES.
Beyond these information-seeking tasks, our model also achieves strong
generalization on the HLE benchmark even though it is only trained on
knowledge-intensive QA data. These results highlight our approach as a
practical path toward long-horizon web agents.