---
title: "BrowserForge：通过并行浏览器沙箱规模化生成网页交互数据"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.24848v1"
permalink: "https://feed.astralor.com/items/itm_5ada8968a57d03a84ece3d99"
published: "2026-08-25T17:35:42.000Z"
score: 81
channel: "ai"
tags: ["BrowserForge","Mind2Web","OpenAI","Web Agent","数据合成"]
---

# BrowserForge：通过并行浏览器沙箱规模化生成网页交互数据

## AI 点评

> 这篇论文解决的是网页智能体训练数据稀缺的实际痛点，开放网页并行沙箱的思路很务实，且数据量和效果提升都很可观。对做Agent数据工程和像素级交互模型的研究者尤其值得关注。

## 速览

BrowserForge 提出一种在大规模开放网页上并行驱动浏览器沙箱来生成网页智能体训练数据的框架。它结合开放网页采集、沙箱集群管理和提议者-求解者双体循环，从真实网页自动生成可执行任务并收集验证轨迹，最终产出了包含 20.3 万条轨迹、每个轨迹来自不同网站的语料库。用该数据微调的紧凑多模态模型在 Online-Mind2Web 上成功率从 25.66% 提升至 33.33%，证明开放网页覆盖和多样化站点选择是性能提升的关键。

## 关键点

- 提出 BrowserForge 框架，利用并行浏览器沙箱在开放网页上大规模生成交互数据
- 构建了 203,238 条轨迹的语料库，每条轨迹来自不同网站，显著提升数据多样性
- 训练后的模型在 Online-Mind2Web 上成功率从 25.66% 提升到 33.33%
- 验证了开放网页采集和广泛站点覆盖是性能提升的核心因素

## 全文

Web agents that act from rendered pixels avoid the fragility and heavy token cost of reading a page's HTML or accessibility tree, but training them depends on large amounts of high-quality interaction trajectories, and how to produce such data at scale remains an open problem. Public datasets typically contain only a few thousand trajectories drawn from a fixed and narrow set of websites, and even recent automated synthesis pipelines stay bound to predefined site lists or tutorial sources, so the number of distinct websites the agent ever sees barely grows. We present BrowserForge, a framework that generates web interaction data at scale by driving many browser sandboxes in parallel over the open web. BrowserForge couples three components: an open-web sourcing stage that exposes the agent to hundreds of thousands of real, openly reachable websites; a sandbox cluster manager that schedules hundreds of concurrent browsers with high utilization; and a Proposer-Solver dual-agent loop that turns a raw page into an executable task and then collects a verified trajectory for it. A rule-plus-model cleaning pipeline removes failed runs and rewrites the surviving reasoning into a single unified chain-of-thought style. Page structure such as the accessibility tree is used only as a synthesis-time signal; the agent we train and release acts purely from the screenshot. The resulting corpus contains 203,238 trajectories, each collected from a distinct website, larger and more diverse than prior trajectory datasets. Fine-tuning a compact multimodal model on this corpus raises its success rate on the live Online-Mind2Web from 25.66% to 33.33% and consistently improves step accuracy on the static Multimodal-Mind2Web, with the gain growing as the corpus scales. Controlled analyses further confirm that open-web sourcing and broad website coverage are key contributors to the observed improvement.

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