Go-Browse: Training Web Agents with Structured Exploration

Table of Contents

• Overview
• Setup
• Collect Dataset
  • Process Collected Go-Browse Dataset for Training
  • Process NNetNav Dataset for Training
• Finetune a Model
• Benchmark a Model on WebArena
• Run an Episode on a Website
• Go-Browse-WA Dataset and Trained Models Release
• Citation

Overview

Go-Browse is a method for automatic, unsupervised collection of high-quality and diverse web agent training data via structured exploration of websites.

Go-Browse has an outer loop that iteratively builds up a graph of previously visited webpages on a website (incentivizing global website coverage) and an inner loop that thoroughly explores each discovered webpage by: (1) Proposing tasks to solve on that page and tasks to discover neighboring pages; (2) Filtering these tasks to feasible ones by trying to solve them and judging successes with a strong computer-use LM + a VLM-as-a-judge and (3) Sampling additional task-solving trajectories with various other pretrained LMs.

By resetting the inner loop to previously discovered webpages, the outer loop helps Go-Browse reuse information across the multiple inner loop invocations, enabling more efficient and deeper exploration of websites.

We release Go-Browse-WA, a dataset collected by running Go-Browse on 100 webpages from WebArena websites, collecting ~10K successful task-solving trajectories and ~17K unsuccessful ones.

Finetuning Qwen-2.5-7B-Instruct on Go-Browse-WA achieves state-of-the-art performance for sub-10B parameter models on the WebArena benchmark with a overall success rate of 21.7%, beating the previous best finetuned sub-10B model by 2.9 percentage points and beating GPT-4o-mini by 2.4 percentage points.

Setup

Note, we ran our experiments with Python 3.12, though earlier python versions may also work.

1. Follow the instructions here to install browsergym with webarena and playwright with chromium: https://github.com/ServiceNow/BrowserGym
2. Install webexp and dependencies:

pip install -r requirements.txt
pip install -e .

3. Setup a WebArena Server using the instructions here: webarena readme. You can also optionally setup the a reset server to remotely reset the webarena environments by:
   • Copy/clone over the webarena-reset folder to your webarena hosting instance
   • pip install fastapi[standard] on this instance.
   • cd webarena-reset
   • export BASE_URL=<PUBLIC URL for your instance>
   • fastapi run reset_server.py
   • You can now reset a specific domain at once (e.g. map with <RESET_SERVER_URL>/reset/map) or all domains at once with (e.g., <RESET_SERVER_URL>/reset/all).

Collect Dataset

Example config file used for Go-Browse-WA data generation is: configs/go_browse_config.yaml

For each domain (website) that you want to run data generation for, duplicate/modify the config file by filling in placeholders and then run:

python -m webexp.explore.algorithms.web_explore -c configs/web_explore_config.yaml

Process Collected Go-Browse Dataset for Training

First, set the input and output paths as appropriate in projects/go-browse/data/generate_dataset.py and projects/go-browse/data/process_dataset.py

Then:

python projects/go-browse/data/generate_dataset.py
python projects/go-browse/data/process_dataset.py

Process NNetNav Dataset for Training

First, set the output path as appropriate in projects/go-browse/data/process_nnetnav_data.py

Then:

python projects/go-browse/data/process_nnetnav_data.py

Finetune a Model

First, replace the placeholder paths/env vars as appropriate in webexp/train/sft_policy.py

Then:

python webexp/train/sft_policy.py

Benchmark a Model on WebArena

If benchmarking a finetuned model, first serve the model using an inference server like vllm or sglang. We used vllm in our experiments.

Duplicate/edit the following config file by filling in the placeholders: configs/benchmark_webarena.yaml.

Then:

python -m webexp.benchmark.run_webarena -c configs/benchmark_webarena.yaml

Run an Episode on a Website

If performing inference with a finetuned model, first serve the model using an inference server like vllm or sglang.

Duplicate/edit the following config file by filling in the placeholders: configs/benchmark_webarena.yaml.

Then:

python -m webexp.agents.run_episode -c configs/benchmark_webarena.yaml

Go-Browse-WA Dataset and Trained Models Release

Datasets (on HF Hub):

• Processed dataset (output of projects/go-browse/data/process_dataset.py): apurvaga/go-browse-wa.

This includes both successful and unsuccessful trajectories processed for finetuning. Page observations are represented as accessibility trees (potentially truncated for context length limits while training).

• Raw dataset: apurvaga/go-browse-wa-raw

Raw version includes screenshots, pruned_html, full accessibility tree text and additional metadata.

Finetuned models (on HF Hub):

• apurvaga/go-browse-wa-qwen-7B
• apurvaga/nnetnav-wa-qwen-7B

Citation

@misc{gandhi2025gobrowse,
      title={Go-Browse: Training Web Agents with Structured Exploration}, 
      author={Apurva Gandhi and Graham Neubig},
      year={2025},
      eprint={2506.03533},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.03533}, 
}