Towards Specialized Web Agents Using Production-Scale Workflow Data
Keywords: LLM web agent
TL;DR: We developed highly effective web agents by fine-tuning open-source LLMs using real-world, high-quality, production-scale workflow data.
Abstract: Large Language Model (LLM) agents are rapidly improving to handle increasingly complex web-based tasks. Most of these agents rely on general-purpose, proprietary models like GPT-4 and focus on designing better prompts to improve their planning abilities. However, general-purpose LLMs are not specifically trained to understand specialized web contexts such as HTML, and they often struggle with long-horizon planning. We explore an alternative approach that fine-tunes open-source LLMs using production-scale workflow data collected from over 250 domains corresponding to 6 billion tokens. This simple yet effective approach shows substantial gains over prompting-based agents on existing benchmarks---our WorkflowAgent achieves state-of-the-art performance on Mind2Web and substantially improves the baseline task success rate from 37.2% to 51.3% on WebArena. We further perform detailed ablation studies on various fine-tuning design choices and provide insights into LLM selection, training recipes, context window optimization, and effect of dataset sizes.
Supplementary Material: zip
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 12501
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