Summary of The Browsergym Ecosystem For Web Agent Research, by Thibault Le Sellier De Chezelles et al.

The BrowserGym Ecosystem for Web Agent Research

by Thibault Le Sellier De Chezelles, Maxime Gasse, Alexandre Drouin, Massimo Caccia, Léo Boisvert, Megh Thakkar, Tom Marty, Rim Assouel, Sahar Omidi Shayegan, Lawrence Keunho Jang, Xing Han Lù, Ori Yoran, Dehan Kong, Frank F. Xu, Siva Reddy, Quentin Cappart, Graham Neubig, Ruslan Salakhutdinov, Nicolas Chapados, Alexandre Lacoste

First submitted to arxiv on: 6 Dec 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The proposed BrowserGym ecosystem aims to streamline the evaluation and benchmarking of web agents, particularly those utilizing Large Language Models (LLMs). By providing a unified environment with standardized observation and action spaces, BrowserGym facilitates reliable comparisons across diverse benchmarks. This paper extends the ecosystem by integrating existing literature-based benchmarks and introducing AgentLab, a framework for agent creation, testing, and analysis. The proposed ecosystem offers flexibility in integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. A large-scale, multi-benchmark web agent experiment is conducted, comparing the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks. Results highlight a significant discrepancy between OpenAI and Anthropic’s latest models, with Claude-3.5-Sonnet leading on most benchmarks, except for vision-related tasks where GPT-4o excels.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The BrowserGym ecosystem helps make it easier to compare different types of web agents that use large language models. Right now, there are many different ways to test and compare these agents, which makes it hard to get accurate results. The authors propose a new way to evaluate and benchmark these agents, by providing a standardized environment with well-defined rules for what the agents can see and do. This will help make it easier to compare different agents and figure out which ones work best. They also introduce a new framework called AgentLab that helps create, test, and analyze web agents. The authors run an experiment comparing 6 different language models on 6 popular benchmarks, and find that one model (Claude-3.5-Sonnet) does particularly well on most tasks.