Is Your LLM Secretly a World Model of the Internet? Model-Based Planning
  for Web Agents
Language agents have demonstrated promising capabilities in automating
web-based tasks, though their current reactive approaches still underperform
largely compared to humans. While incorporating advanced planning algorithms,
particularly tree search methods, could enhance these agents' performance,
implementing tree search directly on live websites poses significant safety
risks and practical constraints due to irreversible actions such as confirming
a purchase. In this paper, we introduce a novel paradigm that augments language
agents with model-based planning, pioneering the innovative use of large
language models (LLMs) as world models in complex web environments. Our method,
WebDreamer, builds on the key insight that LLMs inherently encode comprehensive
knowledge about website structures and functionalities. Specifically,
WebDreamer uses LLMs to simulate outcomes for each candidate action (e.g.,
"what would happen if I click this button?") using natural language
descriptions, and then evaluates these imagined outcomes to determine the
optimal action at each step. Empirical results on two representative web agent
benchmarks with online interaction -- VisualWebArena and Mind2Web-live --
demonstrate that WebDreamer achieves substantial improvements over reactive
baselines. By establishing the viability of LLMs as world models in web
environments, this work lays the groundwork for a paradigm shift in automated
web interaction. More broadly, our findings open exciting new avenues for
future research into 1) optimizing LLMs specifically for world modeling in
complex, dynamic environments, and 2) model-based speculative planning for
language agents.
673e9edfaac00eafcaf4a83c
Yu Gu
673e9edfaac00eafcaf4a83d
Boyuan Zheng
claimed_verified
2025-01-06T16:08:43.883Z
631a95cfa66151e36e54a905
https://cdn-avatars.huggingface.co/v1/production/uploads/631a95cfa66151e36e54a905/jM_4vy9_Kd8zomFVYtsTo.png
Boyuan Zheng
user
boyuanzheng010
673e9edfaac00eafcaf4a83e
Boyu Gou
claimed_verified
2025-01-05T23:03:15.619Z
6500870f1e14749e84f8f887
https://cdn-avatars.huggingface.co/v1/production/uploads/6500870f1e14749e84f8f887/wfvx4BZvh2OyW-vpq5jEy.jpeg
Boyu Gou
user
BoyuNLP
673e9edfaac00eafcaf4a83f
Kai Zhang
673e9edfaac00eafcaf4a840
Cheng Chang
673e9edfaac00eafcaf4a841
Sanjari Srivastava
673e9edfaac00eafcaf4a842
Yanan Xie
673e9edfaac00eafcaf4a843
Peng Qi
673e9edfaac00eafcaf4a844
Huan Sun
673e9edfaac00eafcaf4a845
Yu Su
Is Your LLM Secretly a World Model of the Internet? Model-Based Planning
  for Web Agents
Language agents have demonstrated promising capabilities in automating
web-based tasks, though their current reactive approaches still underperform
largely compared to humans. While incorporating advanced planning algorithms,
particularly tree search methods, could enhance these agents' performance,
implementing tree search directly on live websites poses significant safety
risks and practical constraints due to irreversible actions such as confirming
a purchase. In this paper, we introduce a novel paradigm that augments language
agents with model-based planning, pioneering the innovative use of large
language models (LLMs) as world models in complex web environments. Our method,
WebDreamer, builds on the key insight that LLMs inherently encode comprehensive
knowledge about website structures and functionalities. Specifically,
WebDreamer uses LLMs to simulate outcomes for each candidate action (e.g.,
"what would happen if I click this button?") using natural language
descriptions, and then evaluates these imagined outcomes to determine the
optimal action at each step. Empirical results on two representative web agent
benchmarks with online interaction -- VisualWebArena and Mind2Web-live --
demonstrate that WebDreamer achieves substantial improvements over reactive
baselines. By establishing the viability of LLMs as world models in web
environments, this work lays the groundwork for a paradigm shift in automated
web interaction. More broadly, our findings open exciting new avenues for
future research into 1) optimizing LLMs specifically for world modeling in
complex, dynamic environments, and 2) model-based speculative planning for
language agents.
WebDreamer enhances web agents' performance by using LLMs as world models to simulate action outcomes, leading to significant improvements over reactive baselines.
tree search methods model-based planning large language models world models web environments natural language descriptions VisualWebArena Mind2Web-live speculative planning language agents