WebExplorer-8B: Autonomous Web Navigation LLM

• WebExplorer-8B is an 8-billion parameter LLM agent designed for long-horizon web navigation with extended context (128K tokens) and up to 100 tool-calling turns.
• It employs a systematic data generation and query evolution methodology to synthesize robust QA pairs, supporting both supervised fine-tuning and reinforcement learning.
• The model achieves state-of-the-art performance on diverse benchmarks, demonstrating effective multi-turn reasoning and practical applications in research support and autonomous agent deployment.

WebExplorer-8B is an 8-billion parameter LLM agent designed for long-horizon information-seeking and autonomous multi-step web navigation. Developed via a systematic data generation and evolution methodology, WebExplorer-8B is distinguished by its capacity to perform complex multi-turn interactions—spanning up to 128K context length and supporting up to 100 tool-calling turns—across diverse web-based benchmarks. The model demonstrates state-of-the-art performance at its size on challenging information-seeking tasks, offering practical advancements in agentic LLM design and deployment (Liu et al., 8 Sep 2025).

1. Architectural Foundation and Sequential Design

WebExplorer-8B is architected atop the Qwen3-8B base model, specifically configured for agentic web interaction with long-horizon capabilities. At each discrete time step $t$, the agent produces a ‘thought’ ($\tau_t$) as an intermediate reasoning step, executes a selected action ($\alpha_t$), and receives an observation ($o_t$), aggregating into the explicit execution trajectory

$$H_T = (\tau_0, \alpha_0, o_0, \tau_1, \alpha_1, o_1, ..., \tau_T)$$

This ReAct-like sequential protocol allows the model to retain and incrementally build upon its reasoning and tool usage history, which is essential for maintaining referential continuity and causal consistency in multi-page web search and knowledge synthesis tasks. The architecture is natively compatible with extended context windows (128K tokens) and extended tool calling horizons (100 turns), permitting complex multi-page, multi-query navigation processes.

2. Systematic Data Generation and Query Evolution Methodology

The principal methodological innovation underlying WebExplorer-8B is its use of model-based autonomous exploration and iterative query evolution for dataset synthesis. The initial exploration begins with seed entities (e.g., Wikipedia entries), from which the model autonomously navigates, searching and browsing multiple websites to construct a multi-source information space. This enables direct synthesis of question–answer (QA) pairs that necessitate aggregation and reasoning over disparate content.

Subsequently, the query evolution process is initiated. Unlike data augmentation strategies that simply lengthen or detail queries, evolution proceeds “long-to-short”: salient clues within the query are systematically obfuscated or removed—such as by withholding explicit names or dates—and generic placeholders are introduced. The evolution is formally expressed as iterative updates:

$$H^{(k+1)} = (H^k, \tau_1^{(k)}, \alpha_1^{(k)}, o_1^{(k)}, ..., \tau_{m_k}^{(k)})$$

for $k = 0, ..., K-1$, with the answer preserved but the query progressively rendered more challenging. This process yields high-quality QA pairs demanding deep, multi-step reasoning and non-trivial web navigation.

3. Supervised Fine-Tuning and Reinforcement Learning

WebExplorer-8B undergoes a two-stage training protocol. First, supervised fine-tuning is performed on the evolved QA pairs, with cold-start initialization. The model is trained to decomposing complex questions, invoke search and browse tools, and execute the ReAct protocol with correct modality transitions.

Subsequently, reinforcement learning with a variant of the GRPO algorithm is applied. The reward signal incorporates both format consistency (ensuring structured tool calls and explicit reasoning) and answer accuracy. As RL proceeds, the constraints on context length and tool invocation count are progressively relaxed from $64\text{K}/50$ turns up to $128\text{K}/100$ turns, driving improved chain-of-thought utilization and robust performance across increasingly challenging long-horizon tasks.

4. Benchmark Performance and Comparative Evaluation

WebExplorer-8B was evaluated across a suite of information-seeking and navigation benchmarks:

Benchmark	Accuracy / Success Rate	Comparative Outcome
BrowseComp-en	15.7%	Surpasses WebSailor-72B
BrowseComp-zh	32.0%	Surpasses models ≤72B params
WebWalkerQA	62.7%	Best among models ≤100B
FRAMES	75.7%	Best among models ≤100B
HLE (Academic QA)	17.3%	Out-of-domain generalization

WebExplorer-8B achieves or exceeds the performance of much larger models, demonstrating the effectiveness of data synthesis and RL. After RL training, the model achieves an average of 16 effective tool-calling turns per query, reliably executing multi-step retrieval and reasoning sequences.

5. Generalization Beyond Training Distribution

Although the core data synthesis process is motivated by BrowseComp-style tasks, the QA pairs are ultimately derived from a broad base, including knowledge-rich seed entities and multi-domain sources. WebExplorer-8B’s performance on the HLE benchmark (covering STEM and academic questions) evidences its ability to generalize beyond its supervised training distribution, successfully adapting its multi-turn chain-of-thought and tool usage skills.

6. Practical Implications for Agentic LLM Deployment

The architectural, methodological, and empirical advances embodied in WebExplorer-8B enable new practical horizons for LLM-based web agents. The demonstrated capacity for autonomously conducting long-horizon web navigation with complex multi-source synthesis positions the model for deployment in research support, regulatory compliance, real-time analysis, and general web search environments.

The systematic data creation pipeline—featuring autonomous exploration and query evolution—offers a scalable solution to the data scarcity problem for long-horizon web agent training, which historically impeded agent performance on information-seeking tasks requiring deep navigation and reasoning. A plausible implication is broader adoption of such pipelines for the development of LLM-based agents in domains beyond web navigation, such as scientific literature search, enterprise knowledge management, and dynamic technical support.

7. Significance and Future Directions

WebExplorer-8B highlights the utility of combining extended context processing, systematic hard data synthesis, and finely-tuned RL for efficient agentic model construction. The results indicate that appropriately curated and evolved data, rather than sheer model scale, can drive advances in long-horizon multi-step reasoning and autonomous agent performance.

This suggests a paradigm where future agentic models may be optimized by enriching data complexity and training schemes rather than exponentially growing model parameters. Further research may extend the WebExplorer-8B methodology to diverse graphical user interface environments and integrate additional multimodal signals, cultivating increasingly generalist and capable autonomous agents.

WebExplorer-8B sets quantitative benchmarks for efficient, scalable, and effective long-horizon web navigation at moderate model sizes, establishing methodological precedents for agentic LLM development in academic and applied domains (Liu et al., 8 Sep 2025).