WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning (2509.13305v1)

Abstract: Transcending human cognitive limitations represents a critical frontier in LLM training. Proprietary agentic systems like DeepResearch have demonstrated superhuman capabilities on extremely complex information-seeking benchmarks such as BrowseComp, a feat previously unattainable. We posit that their success hinges on a sophisticated reasoning pattern absent in open-source models: the ability to systematically reduce extreme uncertainty when navigating vast information landscapes. Based on this insight, we introduce WebSailor, a complete post-training methodology designed to instill this crucial capability. Our approach involves generating novel, high-uncertainty tasks through structured sampling and information obfuscation, RFT cold start, and an efficient agentic RL training algorithm, Duplicating Sampling Policy Optimization (DUPO). With this integrated pipeline, WebSailor significantly outperforms all open-source agents in complex information-seeking tasks, matching proprietary agents' performance and closing the capability gap.

• The paper demonstrates that integrating dense, uncertainty-rich synthetic data with a dual-environment RL pipeline bridges the gap between open-source and proprietary web agents.
• The paper presents a novel synthetic dataset, SailorFog-QA-V2, constructed through random walks on dense knowledge graphs to enhance diverse reasoning capabilities.
• The paper validates its approach with state-of-the-art performance across benchmarks, highlighting scalable RL and robust simulation-real world synergy.

WebSailor-V2: Advancing Open-Source Web Agents with Synthetic Data and Scalable RL

Introduction

WebSailor-V2 addresses the persistent performance gap between open-source and proprietary web agents in complex, multi-step information-seeking tasks. The system introduces a comprehensive post-training pipeline that integrates advanced synthetic data construction and a scalable reinforcement learning (RL) framework. The core contributions are twofold: (1) SailorFog-QA-V2, a dataset generated from a densely connected knowledge graph with diverse uncertainty types, and (2) a dual-environment RL setup that leverages both high-fidelity simulation and robust real-world environments, all within a closed-loop data-policy feedback system. The agent, built on Qwen3-30B-A3B, achieves state-of-the-art results on challenging benchmarks, outperforming both open-source and much larger proprietary models.

Data Construction: SailorFog-QA-V2

The SailorFog-QA-V2 dataset is constructed to overcome the limitations of prior data generation methods, which typically yield acyclic, tree-like knowledge structures and rely on narrow definitions of uncertainty (primarily obfuscation). The new pipeline constructs a dense knowledge graph by actively seeking cyclic and highly interconnected subgraphs, better reflecting real-world information webs. Subgraph extraction employs a random-walk-based approach, ensuring efficient sampling of non-isomorphic, structurally diverse subgraphs. QA generation is further diversified by introducing multiple uncertainty types beyond obfuscation, targeting a broader spectrum of reasoning skills.

This approach ensures that the agent is exposed to a wide variety of logical relationships and uncertainty scenarios, which is critical for generalization and robust reasoning in open-domain web tasks.

Agentic Framework and Tooling

WebSailor-V2 adopts the ReAct framework for agentic reasoning, prioritizing simplicity and scalability over complex, human-engineered planning. The agent's action space comprises four primary tools: search, visit, Google Scholar, and a Python interpreter, in addition to a terminal "final answer" action. This toolkit enables the agent to perform iterative, tool-augmented reasoning, dynamically gathering and synthesizing information from the web and academic sources, and executing code for computation when necessary.

Scalable Reinforcement Learning Pipeline

The RL pipeline is designed for both scalability and robustness. It consists of two tightly integrated environments:

• Simulated Environment: Built on an offline Wikipedia corpus, this environment enables high-frequency, low-cost experimentation and data curation. The simulation closely mirrors real-world tool dynamics, state transitions, and reward mechanisms, facilitating rapid algorithmic iteration.
• Real-World Environment: The agent interacts with live web APIs, including search engines and web page parsers. A unified tool execution interface abstracts away API volatility, employing concurrency management, fault tolerance, and result caching to ensure deterministic and stable tool invocation.

A fully automated data synthesis and filtering pipeline dynamically curates the training set based on training dynamics, closing the loop between data generation and policy learning.

Figure 1: The RL framework integrates simulated and real environments with an automated data synthesis and filtering pipeline for closed-loop training.

RL Algorithm and Training Dynamics

The RL algorithm is a variant of GRPO, optimized for token-level policy gradients and employing a leave-one-out strategy for advantage estimation. Negative samples are conservatively filtered to maintain training stability, and large batch/group sizes are used to reduce variance. The training regimen is strictly on-policy, with trajectories sampled from the latest policy.

Empirical results indicate that data quality and environment stability are more critical than algorithmic details for successful agentic RL. Notably, training directly on human-annotated benchmarks (e.g., BrowseComp) yields inferior results compared to synthetic data, likely due to the latter's more consistent distribution and learnable structure.

Figure 2: RL training dynamics show consistent reward improvement and validation performance across benchmarks.

Experimental Results

WebSailor-V2-30B-A3B achieves strong results across multiple benchmarks:

• BrowseComp-EN: 35.3
• BrowseComp-ZH: 44.1
• xBench-DeepSearch: 73.7
• GAIA: 74.1
• HLE: 30.6

These results surpass all open-source agents and, in several cases, outperform proprietary models with significantly larger parameter counts (e.g., DeepSeek-V3.1-671B). The SFT cold-start stage is shown to be indispensable, providing a robust initial policy that enables effective RL exploration in sparse-reward, long-horizon tasks.

Figure 3: WebSailor-V2 achieves state-of-the-art performance on BrowseComp-EN and xBench-DeepSearch, outperforming both open-source and proprietary agents.

Analysis: Training and Inference Behavior

Training and Entropy Dynamics

Training curves reveal that RL substantially improves both pass@1 and pass@3 on difficult benchmarks, indicating genuine expansion of problem-solving capacity. On simpler tasks, RL mainly increases sampling efficiency. Policy entropy remains high throughout training, reflecting sustained exploration due to the non-stationary, stochastic nature of web environments. No explicit entropy regularization is required.

Context Scaling

Increasing the context window from 32k to 128k tokens and allowing up to 100 ReAct iterations yields significant accuracy gains, with diminishing returns beyond 64k context. This demonstrates the importance of long-context modeling for complex, multi-step reasoning.

Figure 4: (Left) Accuracy increases with context length and tool call budget; (Right) RL yields accuracy improvements across four benchmarks.

Case Study: Complex Multi-Step Reasoning

A detailed BrowseComp case paper demonstrates the agent's ability to decompose complex queries, adapt search strategies, identify key information pivots, and systematically verify hypotheses using multiple tools. The agent's workflow mirrors expert human research methodologies, including iterative hypothesis generation, targeted information retrieval, and cross-source verification.

Figure 5: Stepwise reasoning and tool use in a complex BrowseComp case.

Figure 6: Continued reasoning and evidence synthesis in the same case.

Figure 7: Final answer synthesis with comprehensive citation and justification.

Implications and Future Directions

WebSailor-V2 demonstrates that with high-quality synthetic data and a robust RL pipeline, open-source models of moderate scale can match or exceed the performance of much larger proprietary systems in agentic web research tasks. The results underscore the primacy of data and environment engineering over algorithmic novelty in current agentic RL. The modular, scalable framework paves the way for further advances in:

• Automated, closed-loop data generation and filtering
• More sophisticated uncertainty modeling in QA generation
• Integration of advanced context management and memory modules
• Transfer to other domains requiring tool-augmented, long-horizon reasoning

Conclusion

WebSailor-V2 establishes a new standard for open-source web agents by combining dense, uncertainty-rich synthetic data with a scalable, stable RL pipeline. The system achieves state-of-the-art results on challenging benchmarks, closing the gap with proprietary agents and demonstrating that careful system engineering—particularly in data and environment design—is the key determinant of agentic performance. This work provides a robust foundation for future research in scalable, tool-augmented AI agents.