AIAny

WebArena: A Realistic Web Environment for Building Autonomous Agents

Tests whether browser agents complete 812 tasks across realistic shopping, forum, development, and content-management sites. Evaluates final outcomes rather than matching a reference sequence of clicks.

Introduction

A plausible browsing trace can still leave the user's request unfinished. The central insight is to judge the resulting website state, which lets researchers compare agents even when they take different routes to the same goal.

Key Findings
  • The paper reports 14.41% success for its best GPT-4 baseline versus 78.24% for humans, exposing a large gap between fluent instructions and reliable execution. These are the paper's baseline results, not current leaderboard scores.
  • 812 tasks span information retrieval, navigation, and changes to site content or settings. This tests sustained execution beyond isolated page actions.
  • Self-hosted sites and resettable state support repeatable comparisons; outcome validators allow multiple valid solutions. Some answer checks use model-based semantic matching.
  • Unachievable requests test whether an agent recognizes missing information or permissions instead of inventing success.
Who Should Read It

Great fit if you study browser-agent planning, observation representations, or failure recovery and need reproducible experiments. Look elsewhere if you need direct evidence of reliability on arbitrary live websites: the controlled sites omit much of the open web's changing content, authentication, and anti-bot friction. Hosting and resetting the environment also adds operational work.

Information

  • Websitearxiv.org
  • OrganizationsCarnegie Mellon University
  • AuthorsShuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried …
  • Published date2023/07/25

Categories

More Items

Embeds an agent's reasoning together with its search query to improve retrieval. Trains a 4B embedding model on synthesized deep-research trajectories, helping preserve intent across multi-step searches.

Trains an 8B model to search, synthesize evidence, and write cited research reports. Its reinforcement-learning rewards use evolving rubrics grounded in retrieved evidence and contrasting model responses.

Trains long-horizon web researchers by combining agentic mid-training, synthetic trajectories, and reinforcement learning. A 30.5B-parameter model activates 3.3B parameters per token and learns to plan, search, and synthesize evidence.