BrowserForge: Scaling Web Episodes via Parallel Browser Sandboxes
Score
81提出了大规模开放网页数据合成新方法，语料库规模和多样性远超现有数据集。
方法设计完善，包含消融实验和多维度评估，但部分实现细节未完全公开。
可直接提升网页智能体训练效果，对实际Agent开发具有较强参考价值。
发布日期为2026年8月，属于最新研究进展，契合当前Agent训练数据需求。
论文来自arXiv AI/CL/LG，属于预印本但有完整实验支撑，可信度较高。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
Significant gains in data scale and diversity with thorough experiments, pushing the web agent field forward.
AI commentary
「This paper tackles the real pain point of scarce web agent training data with a pragmatic parallel-sandbox approach, showing substantial gains in scale and performance. It is especially valuable for researchers working on agent data engineering and pixel-based interaction models.」
Quick overview
BrowserForge presents a framework that generates web agent training data by driving many browser sandboxes in parallel over the open web. It combines open-web sourcing, sandbox cluster management, and a Proposer-Solver dual-agent loop to turn raw pages into executable tasks and collect verified trajectories, yielding a corpus of 203,238 trajectories, each from a distinct website. Fine-tuning a compact multimodal model on this corpus raises its success rate on live Online-Mind2Web from 25.66% to 33.33%, with controlled analyses confirming that open-web sourcing and broad website coverage are key contributors.
- Proposes BrowserForge, a framework that uses parallel browser sandboxes to generate interaction data at scale over the open web
- Builds a corpus of 203,238 trajectories, each from a distinct website, significantly improving data diversity
- Fine-tuned model improves success rate on Online-Mind2Web from 25.66% to 33.33%
- Identifies open-web sourcing and broad website coverage as key drivers of improvement
Full text
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