Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents

• URL: http://arxiv.org/abs/2411.06559v2
• Date: Tue, 01 Apr 2025 05:04:47 GMT
• Title: Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents
• Authors: Yu Gu, Kai Zhang, Yuting Ning, Boyuan Zheng, Boyu Gou, Tianci Xue, Cheng Chang, Sanjari Srivastava, Yanan Xie, Peng Qi, Huan Sun, Yu Su,
• Abstract summary: We propose a model-based planning framework for web agents that employs a world model to simulate and deliberate over the outcome of each candidate action before committing to one.<n> Empirical results demonstrate that WebDreamer achieves substantial performance improvements over reactive baselines.<n>Our trained world model, Dreamer-7B, performs comparable to GPT-4o, highlighting the potential of specialized world models for efficient and effective planning in complex web environments.
• Score: 22.608219492706876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language agents based on large language models (LLMs) have demonstrated great promise in automating web-based tasks. Recent work has shown that incorporating advanced planning algorithms, e.g., tree search, is advantageous over reactive planning for web agents. However, unlike simulated sandbox environments, real-world environments such as the web are rife with irreversible actions. This undermines the feasibility of backtracking, a cornerstone of (tree) search. Overly relying on test-time search also hurts efficiency. We advocate model-based planning for web agents that employs a world model to simulate and deliberate over the outcome of each candidate action before committing to one. We systematically explore this paradigm by (1) Proposing a model-based planning framework, WebDreamer, which employs LLMs to serve as both world models and value functions; (2) Training specialized LLMs as world models with a scalable data synthesis pipeline. Empirical results demonstrate that WebDreamer achieves substantial performance improvements over reactive baselines. It is competitive, while being 4-5 times more efficient, with tree search in sandbox environments (VisualWebArena) and also works effectively on real-world websites (Online-Mind2Web and Mind2Web-Live). Furthermore, our trained world model, Dreamer-7B, performs comparable to GPT-4o, highlighting the potential of specialized world models for efficient and effective planning in complex web environments.

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