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BrowserForge framework generates web agent training data at scale

By PulseAugur Editorial · [1 sources] ·

Researchers have developed BrowserForge, a framework designed to generate large-scale web interaction data for training web agents. This system utilizes hundreds of parallel browser sandboxes to explore the open web, creating over 200,000 unique interaction trajectories. By training a multimodal model on this diverse dataset, the agent's success rate on the Online-Mind2Web benchmark increased from 25.66% to 33.33%, demonstrating the effectiveness of broad website coverage and parallel data synthesis. AI

IMPACT BrowserForge's approach to generating diverse web interaction data could significantly improve the capabilities and robustness of web agents.

RANK_REASON The item describes a new framework and dataset for training AI agents, presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

BrowserForge framework generates web agent training data at scale

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3 / 100
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The item describes a new framework and dataset for training AI agents, presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    BrowserForge: Scaling Web Episode via Parallel Browser Sandboxes

    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. Pu…

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