WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents
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.
68bfb768207285de11b07d02
Junteng Liu
68bfb768207285de11b07d03
Yunji Li
claimed_verified
2025-09-09T13:46:35.234Z
62ac3bcec35bb36ff0785962
/avatars/edd4f14556abc39739bac951043a3065.svg
李云济
user
awdrgyjilplij
68bfb768207285de11b07d04
Chi Zhang
68bfb768207285de11b07d05
Jingyang Li
68bfb768207285de11b07d06
Aili Chen
claimed_verified
2025-09-09T13:46:37.221Z
63f86b099f87cc3e645b51d9
/avatars/27ca5ba425640bf67474cee871e8e53a.svg
Ellie Chen
user
sheep33333
68bfb768207285de11b07d07
Ke Ji
claimed_verified
2025-09-12T16:19:51.076Z
64c24a1b3499e4f3a2fb680c
/avatars/7859c3164071a1797c4d9271c8c6ac2e.svg
Ke Ji
user
kehorizon
68bfb768207285de11b07d08
Weiyu Cheng
68bfb768207285de11b07d09
Zijia Wu
68bfb768207285de11b07d0a
Chengyu Du
68bfb768207285de11b07d0b
Qidi Xu
68bfb768207285de11b07d0c
Jiayuan Song
68bfb768207285de11b07d0d
Zhengmao Zhu
68bfb768207285de11b07d0e
Wenhu Chen
68bfb768207285de11b07d0f
Pengyu Zhao
68bfb768207285de11b07d10
Junxian He
WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents
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.
WebExplorer, a data-driven approach for developing advanced web agents, achieves state-of-the-art performance in information-seeking tasks through systematic data generation and reinforcement learning.
Large Language Models agentic applications web browsing capabilities information-seeking abilities model-based exploration iterative query evolution query-answer pairs multi-step reasoning complex web navigation supervised fine-tuning reinforcement learning context length tool calling turns long-horizon problem solving BrowseComp-en/zh WebWalkerQA FRAMES HLE benchmark