---
title: "A Real-World WebAgent with Planning, Long Context"
description: "Gur et al. propose WebAgent, which splits the work between two models. 89 references, cited by 106 papers here."
authors: ["Izzeddin Gur", "Hiroki Furuta", "Austin Huang", "Mustafa Safdari", "Yutaka Matsuo", "Douglas Eck", "Aleksandra Faust"]
year: 2023
venue: "arXiv:2307.12856"
source: "https://arxiv.org/abs/2307.12856"
doi: "10.48550/arxiv.2307.12856"
arxiv: "2307.12856"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/gur-2023-real-world-webagent-planning-long/"
---
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# A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis

**Authors:** Izzeddin Gur, [Hiroki Furuta](https://md.research.elamur.ai/free/en/authors/hiroki-furuta/), Austin Huang, [Mustafa Safdari](https://md.research.elamur.ai/free/en/authors/mustafa-safdari/), [Yutaka Matsuo](https://md.research.elamur.ai/free/en/authors/yutaka-matsuo/), [Douglas Eck](https://md.research.elamur.ai/free/en/authors/douglas-eck/) and [Aleksandra Faust](https://md.research.elamur.ai/free/en/authors/aleksandra-faust/) · **Year:** 2023 · **Venue:** arXiv:2307.12856 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2307.12856) · [PDF](https://arxiv.org/pdf/2307.12856) · [DOI 10.48550/arxiv.2307.12856](https://doi.org/10.48550/arxiv.2307.12856) · [arXiv 2307.12856](https://arxiv.org/abs/2307.12856) · [OpenAlex](https://openalex.org/W4385262076)

**Citations:** 14 citations (OpenAlex, 2026-10-14) · [106](https://md.research.elamur.ai/free/en/papers/gur-2023-real-world-webagent-planning-long/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Agents driven by large language models do well on simulated web environments but struggle on real websites. Real sites allow actions that cannot be listed in advance, their HTML pages run to many thousands of tokens, and general-purpose LLMs know little about HTML structure. Gur et al. propose WebAgent, which splits the work between two models. HTML-T5 is a new encoder-decoder model pre-trained on HTML extracted from CommonCrawl. It uses local and global attention and a denoising objective that masks longer spans. Given the user's request, HTML-T5 breaks it into step-by-step sub-instructions and reduces each page to the fragments relevant to the current step. Flan-U-PaLM (540B) receives these outputs through few-shot prompts and writes Python (Selenium) code that the browser executes. HTML-T5 is fine-tuned on data the agent collects itself, called self-experience supervision. On live websites, this modular design raises task success by more than 50%. HTML-T5 alone also sets strong results on MiniWoB++ and Mind2Web. The work shows that specialised long-context HTML models combined with code generation can make web agents practical outside simulators.

## Key points
- Real-site HTML averages about 7K–14K tokens, compared with roughly 0.5K in simulators. Pre-training HTML-T5 with a mean span length of µ=8 instead of the usual 3 hides more meaningful pieces of HTML, which helps the model learn its structure.
- On 56 MiniWoB++ tasks, HTML-T5-XL fine-tuned on 347K demonstrations reaches an average success of 0.856. This compares with 0.755 for Flan-T5-XL and 0.484 for the earlier WebN-T5-XL, an 18.7% gain over prior language-model agents.
- On the offline Mind2Web benchmark, HTML-T5 achieves state-of-the-art results. It outperforms Synapse (GPT-3.5) and MindAct (Flan-T5-XL and GPT-4).
- Error analysis indicates that pairing planning with HTML summarisation in a specialised model is key to success on real websites.
- Swapping the code-writing model on a map website (20 instructions) gives these success rates:
  - Flan-U-PaLM-540B: 80%
  - gpt-3.5-turbo: 80%
  - Flan-PaLM-62B: 60%
  - Flan-PaLM-8B: 0%

  This suggests any LLM that writes code well enough can fill this role.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [The University of Tokyo](https://md.research.elamur.ai/free/en/universities/the-university-of-tokyo/) — University, Tokyo, Japan · 96 works · 9,600 citations

📚 Shelves (3): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works

🧭 Topics (1): [💻 Code & programming languages](https://md.research.elamur.ai/free/en/topics/code-and-programming/) · 168 works

## Cite

Cite as: Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck and Aleksandra Faust. 2023. A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis. arXiv:2307.12856.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{gur2023real,
  title = {{A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis}},
  author = {Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck and Aleksandra Faust},
  year = {2023},
  doi = {10.48550/arxiv.2307.12856},
  eprint = {2307.12856},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2307.12856}
}
```

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*Updated 2 November 2026*