WebFactory: Automated Compression of Foundational Language Intelligence into Grounded Web Agents
Authors: Sicheng Fan
Qingyun Shi
Shengze Xu
Shengbo Cai
Tieyong Zeng
LI LING
Yanyi Shang
Dehan Kong
Venue: ICLR 2026 Poster (2026)
Keywords: web agent
offline web environment
benchmark
reinforcement learning
synthetic data
GUI grounding
Primary area: datasets and benchmarks
TL;DR: An open-source, fully controllable offline web environment whose built-in site knowledge drives a pipeline to generate executable tasks and high-quality RL data, significantly boosting web-agent performance.

Abstract: Current paradigms for training GUI agents are fundamentally limited by a reliance on either unsafe, non-reproducible live web interactions or costly, scarce human-crafted data and environments. We argue this focus on data volume overlooks a more critical factor: the efficiency of compressing a large language model's (LLM) latent knowledge into actionable agent behavior. We introduce WebFactory, a novel, fully automated closed-loop reinforcement learning pipeline for GUI agents, systematically compressing LLM-encoded internet intelligence into efficient, grounded actions. Our pipeline features a process of scalable environment synthesis → knowledge-aware task generation → LLM-powered trajectory collection → decomposed reward RL training → systematic agent evaluation.
Remarkably, our agent demonstrates exceptional data efficiency and generalization. Trained on synthetic data from only 10 websites within WebFactory, it achieves performance comparable to GUI agents trained on same amount of human-annotated data from a much larger set of environments. This superior performance is consistent across our internal offline and online transferring benchmarks, where our agent also significantly outperforms the base foundation model.
We further provide critical insights into the "embodiment potential" of different LLM foundations, offering a new axis for model evaluation. This work presents a scalable and cost-effective paradigm for transforming passive internet knowledge into active, grounded intelligence, marking a critical step towards general-purpose interactive agents.

Published as a conference paper at ICLR 2026
AUTOMATED COMPRESSION OF FOUNDATIONAL LANGUAGE INTELLIGENCE INTO GROUNDED WEB AGENTS
Web Factory:
Sicheng Fan 122 Qingyun Shi Shengze Shengho Cai?.4, Tieyong Zeng", Li Ling', Yanyi Shang", Dehan Kong
Fudan University MMea Al
The Chinese University of Hong Kong
'Tsinghua University
ABSTRACT
Current paradigms for training GUI agents are fundamentally limited by reliance on either unsafe, non-reproducible live web interactions or costly, scarce human- crafted data and environments. We argue this focus on data volume overlooks a more critical factor: the efficiency of compressing a large language model's (LLM) latent knowledge into actionable agent behavior. We introduce WebFac- tory, novel, fully automated closed-loop reinforcement learning pipeline for GUI agents, systematically compressing LLM-encoded internet intelligence into efficient, grounded actions. Our pipeline features process of scalable envi- romment synthesis - knowledge aware task generation - LLM-powered trajec- tory collection decomposed reward RI. training systematic agent evalua- tion. Remarkably, our agent demonstrates exceptional data efficiency and gener- alization. Trained on synthetic data from only 10 websites within WebFactory. it achieves performance comparable to GUI agents trained on same amount of noman--nnnottedd data from a much larger set of environments. This superior per- formance is consistent across our internal offline and online transferring bench- marks, where our agent also significantly outperforms the base foundation model. We further provide critical insights into the "embodiment potential" of different LIM foundations, offering a new axis for model evaluation. This work presents a scalable and cost-effective paradigm for transforming passive internet knowledge into active, grounded intelligence, marking a critical step towards general-purpose purpose interactive agents.
INTRODUCTION
The advent of Large Language Models (LLMs) has marked a paradigm shift, creating what we term "internet-scale intelligence" the rich world model and reasoning capabilities compressed from the vast internet corpus (Ouyang et al.) 2022). Yet, this intelligence remains descriptive, not action- able. While an LLM's knowledge represents powerful compression of digital experience, an embodied agent's single action in a GUI-a click or keystroke-is an exponentially deeper com- pression, translating abstract intent into tangible environmental change. Bridging this fundamental "semantic-to- action gap" is the central challenge in creating capable GUI agents; LLMs know about GUI interactions. they lack the grounding reliably perform them in complex and dynamic GUI environments (Shi et al. 2017 Liu et al. 2018
Chezelles et al. 2024).
Current attempts to bridge this gap are caught in a dilemma between scalability and control. On one hand, reliance on human labor presents two-fold bottleneck: beyond the immense cost and inherent biases of annotating thousands of trajectories (Deng et al. 2023 Luo et al., 2025), the painstaking, manual synthesis of high-fidelity environments can itself consume weeks of expert effort. On the other hand, training on the live web offers scale but sacrifices control; is a chaotic environment where non-determinism, safety risks, and noise present formidable barriers reproducible research (Zhou et al., 2024] Miyai et al.] 2025) Garg et al.) 2025). As a result, neither approach offers a sustainable path toward creating truly scalable and robust agents.

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Published as a conference paper at ICLR 2026
To overcome these limitations, we argue for paradigm shift: instead of treating LLMs mere components to be fine-tuned, we can leverage them as the architects of their own embodiment. We introduce the concept of Intelligence Compression Factory: a closed-loop, end-to-end pipeline that systematically transforms the descriptive, internet-scale intelligence of LLMs into grounded, actionable behavior. As shown in Figure | this factory operates not the noisy live weh(Pan et al. 2024), but within a high-fidelity, fully observable offline environment. Replicating real-world websites in this manner eliminates non-determinism and safety concerns, thereby creating the ideal conditions for our factory to operate. Here, an LLM-driven, knowledge-awee task synthesizer can generate virtually infinite stream of diverse and executable tasks, shattering the bottleneck of human annotation.
Our key contributions are the design, implementation, and validation of this factory:
High-Fidelity Offline Web Environment: An open-source and reproducible suite that faithfully replicates production websites, providing strict controllability and full observability while elimi- nating noise, privacy concerns, and non-determinism inherent to live web interaction.
Knoviedge-Driven Task Generation: A mechanism that leverages environment observability and LLM knowledge to automatically synthesize diverse, executable, and unbiased task instruc- tions with unambiguous ground-truth answers, removing reliance on costly human annotation.
Scalable Trajectory Generation: Integration of strong LLM executors (e.g., OpenAl's srmpuuter-us-previie within the controlled environment to generate large-scale, high-quality interaction trajectories. A filtering process ensures reproducibility and correctness, while novel "behavioral intent alignment feedback" further enhances information retrieval tasks.
Reinforcement Learning with Unified Action Space and Decomposed Reward: An RL train- ing framework supporting GRPO and related algorithms. We design unified action space and decomposed reward function that combines structural format validation with fine-grained accuracy (action type, click point, input text). For retrieval tasks, normalized Fl-based scoring stabilizes optimization and improves robustness (Christiano et al. 2017: Ouyang et al.) 2022).
Robust Evaluation Protocols: Comprehensive evaluation at both the task level (via key-node tracking) and sub-task level (via grounding metrics), enabling systematic and reproducible assess- ment of agent capabilities.
Open-Sourced Toolchain: fully released, extensible toolkit including environments, task gen- erators, training pipeline, and evaluation tools, supporting scalable and reproducible research across diverse web domains.
Agents trained in WebFactory exhibit superior performance and data efficiency. On our internal offline and online benchmarks, they consistently outperform both the base foundation model and existing agents trained on equivalent volumes of comman-annotade strikingly, despite being trained on only 10 websites, our agent achieves competitive performance on general benchmarks against counterparts trained on much broader corpus of human data. This success offers compelling evidence for the "intelligence compression" philosophy.
datagLuo et al.) 2025). More
Beyond empirical performance, we introduce the concept of "LLM embodiment", quantifying how effective foundation LLM tokens are transformed into grounded agent intelligence. Our anal- ysis reveals that different foundation models possess varying potential for embodiment, offering a new axis for model evaluation. Our findings also highlight the critical roles of full environment observability in maximizing training efficacy.
In summary, this work presents a scalable, safe, and cost-effective approach to transforming LLMs' descriptive intelligence into actionable GUI agent behaviors. We further propose that the agent scaling law should be refined beyond data volume to account for a model's efficiency in intelligence compression and its inherent capability for embodiment. While validated here in GUI settings, this paradigm holds strong promise for more complex physical embodied environments (Chevalier- Boisvert al.) 2018 Shridhar et al. 2020
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WebFactory: Intelligence Compression Pipelinee
Step
High-Fidelity Offline Env & Task Generation
Safe Controliable
Intelligence Compression
LLM
LLM Observes
Knowledgs-Srive Oriven Teak Generstor
High-Fidellty Environment (Fully Oiseeratio)
Auto-Task Synthesis
Embodiment
Step 2
Scalable Trajectory Generation
Gap
Filter/Feedback
Compression Input
Teacher
Foundation LLM
(Stmin L.M
Filtering Process
Intert Alghment Feedbark
High-Quality Trajactories
(Internet- Scale Intelligence)
Grounded Agent
Descriptive Knowlodge
Step
RL Training with Unified Action & Reward
Actionable Behavior
Unified Action Space
Decomposed Reward Fundton
Superior Generalization
Typo
Click Point
Azuracy Hewar
Student
Input Text
Bucoees Reward
Closed-lop
Reward
Overview of the Web which compresses foundation model intelligence into grounded GUI agents through three stages: high-fidelity offline environment & task synthesis, scal- able trajectory generation, and unified-action RL training.
Figure 1:
2 METHOD
2.1 A HIGH-FIDELITY, FULLY CONTROLLABLE WEB ENVIRONMENT
To enable scalable data generation and automated RL training for web agents, we develop fully controllable offline environment that preserves the structural richness of production sites while guar- anteeing strict reproducibility. A central component is our L.LM-assisted synthesis pipeline, which automatically generates realistic websites-includdng layouts, workflows, and content-enabling low-cost, rapid expansion of training domains without manual engineering. This design achieves three critical objectives:
(i) cost-effective synthesis of large-scale, high-quality training data, (ii) safe and systematic RL experimentation without real-world consequences, and (iii) stable, versioned benchmarks for reproducible evaluation.
The environment eliminates common deployment obstacles: sites boot into pre-authenticated sessions with seeded profiles, bypassing login/MFA requirements; anti-automation defenses (CAPTCHA, bot detection) are disabled isolate agent capabilities; and all content versioned in static datasets (e.g., 'Data. js') for exact reproducibility. Full access to frontend code, databases, and interaction logic facilitates rapid iteration and instrumentation.
We curate ten site families spanning key web activities: e-commerce, information search, travel planning, employment, communication, and enterprise services. These sites feature diverse UI pat- terns-from simple forms to drag-and- -nng-p interfaces and hover-triggered menus- providdng com- prehensive coverage of web interaction paradigms.
The entire codebase is open-source, enabling researchers to extend the site collection implement custom tasks. Task difficulty is adjustable across data complexity (catalog size, network density), UI complexity (multi-level navigation, drag-and-drop, hover menus), and workflow depth (from simple lookups to multi-step executions). This flexibility supports targeted evaluation of key competencies: information retrieval, form completion, navigation efficiency, and constraint- decision-making making.
Pipeline integration. The environment supports the end-to-end training pipeline described in Sec. 2.2 It exposes ground-truth data and site knowledge for task synthesis, enforces trajectory correctness during generation, and enables automatic reward computation for RL. For information- retrieval tasks, canonical answers are directly accessible from the data layer. This infrastructure serves both as data-generation platform and versioned benchmark for reproducible evaluation.
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Flights (Trip)
Shopping (E (E-commerce)
CarRental (Car Hire)
DoorDash (Food Delivery)
(Communication)
MaskerTicket (Ticketing)
Figure 2: Representative offline websites from our curated environment (6 of 10 shown).
2.2
KNOWLEDGGE-RRIVE RL TRAINING PIPELINE FOR WEB AGENTS
2.2.1
KNOWLEDGE PRESERVATION & TASK GENERATION
Knowledge-driven task generation. A critical advantage of fully observable environment is the ability to guarantee task validity and answerability. For each site, we extract a machine-readable readable knowledge specification capturing: (i) the navigation graph with permissible page transitions, (ii) page-level semantics and affordances, and (iii) canonical interaction flows (e.g.. browse - detail + cart). This complete observability eliminates common data generation pitfalls-tasks referenc- ing non-existent pages, unavailable information, infeasible actions are prevented by design.
Leveraging this knowledge, we generate two complementary task families:
(a) Operation tasks evaluate long-horizon interaction competence through state-changing actions (e.g., "Add iPhone 17 with 256GB storage to cart"). These are synthesized by traversing the naviga- tion graph ensure all generated procedures are executable on the actual site.
(b) Information-retinnn servable data layer (c.g., "What are Cafe A's weekend hours?"), Since all site data is accessible, verify answer availability before task generation and compute the exact navigation path required for retrieval. This approach produces unambiguous ground-truth answers essential for both supervised learning and automated reward computation (see Listing for an example).
tasks pose queries with guaranteed answers drawn directly from the ob-
The full observability thus transforms traditionally unreliable task generation into deterministic process, ensuring every synthesized task is both executable and verifiable-a prerequisite for scal- able training and evaluation.
2.2.2 BATCH DATA GENERATION AT SCALE
Given a predefined task set, we use strong executor (OpenAl's computer-use-rreveer within this offline enviromment to execute tasks and collect trajectory data. A filtering pipeline removes low-quality traces via (i) state-replay checks, (ii) key-node coverage, and (iii) answer validation for retrieval tasks. In addition, site-exposed auxiliary knowledge assists the executor and enables extra consistency checks, improving both accuracy and yield. Together, these properties make large-scale trajectory generation routine and low-cost while preserving reproducibility: the result scalable corpus of high-quality data suitable for SFT, offline RL, or hybrid training. Appendix F provides a detailed description of Trajectory Dataset Statistics Distributions.
2.2.3 REINFORCEMENT LEARNING FROM GENERATED TRAJECTORIES
We build upon the GUI-RI framework (uo et al. 2025) and extend it to support information retrieval tasks in web environments. While the original framework focuses action-oriented
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"id": "task_retrieval_e177 "site": "MealDash",
"start_url": "/mealdash",
"goal": "Search for Cafe A, open detail page, and tell the Sunday opening time, formatted as HH:MM in 24-hour style.", "
"expected_answers".. [
"11:00",
"11 am",
"opens at 11:00"
1.
"key_nodes": [
"search_box" box",
"resul list", "cafe_detail_page"
]
Listing 1: Example schema for a retrieval task
GUI manipulation, we adapt it to handle data acquisition tasks by introducing a specialized get_final_answer answer action and corresponding reward mechanisms for answer evaluation.
We optimize a policy for web-based GUI agents operating in a structured action space. Each action step is a tuple
a, {a,t point
rent
where at E fclick, double_cliiiiee type, scroll, keypress, drag, get_final_answer denotes v1l,
the action type, of = [x, y] (or [[r,, [I2, y2ll for drag), and alt contains input text or di- rectional parameters (e.g., UP/DOWN for scroll). Generated trajectories populate replay buffer (St, a, S1+1)
Reward. Let R2 be the format reward and Raccurcy [0, be the task-specific accuracy reward. The per-step reward is
R = a: R2 + B Raccurscy
where a, are weighting coefficients.
Accuracy Reward. We employ hierarchical validation: action type must match before evaluating
action-specific parameters. Let A = {click, type, scroll, drag, get_answer,...) be the action The accuracy reward is:
0g
if tre * ge
coadd gebboxx,
if ahe e {click}
[[Fi (atex gglee)) > 7],
if fre e (type, scroll)
Race
(3)
max,ER [[Fi (alest ,r) 7].
if fre get_answer
Illladuu - ggdrug 2 S.
TT drag otherwise
where 7 = 0.5 is the Fl threshold, e the drag tolerance, and R = {ri,..., rK} contains equivalent answers for retrieval tasks. Text comparison uses normalization norm(-) for casseppunctuation/format invariance.
Format reward. Ry validates the structural integrity: proper JSON formatting, valid action types from the web action set, appropriate parameter types, and conditional requirements (c.g.. text re- quired for type actions, directional strings for scroll).

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2.2.4 CLOSED-LOOP PIPELINE
We integrate the controllable environment (Sec. 2.1 knowledge & task generation (Sec. 2.2.1 large-scale trajectory collection (Sec. (.2.2). and RL training (Sec. 2.2.3) into open, fully script- able pipeline that operates with minimal human oversight.
The pipeline proceeds follows: (1) Knowledge & data materialization: for each site, construct knowledge pack comprising the navigation graph, page semantics and affordances, canonical flows, and explicit data snapshot for downstream use; (2) Task synthesis: combine template- and LLM- based generation, with automatic validators (schema, visibility, reachability) to produce the task set T: (3) Trajectory generation and filtering: execute T with a strong agent the offline suite, enforcing deterministic replay, key-node coverage, and answer checks to yield the replay buffer B (4) RL training: optimize Ty the unified action space to maximize J(0) using decomposed reward that combines format validation with fine-grained accuracy (action type, click location, input text), with retrieval answers scored by normalized Fi; and (5) Evaluation: scripted replays with ay-noode-lignen process metrics and normalized answer matching, eliminating the need for human raters.
EXPERIMENTS
We conduct comprehensive experiments validate the effectiveness of our knowledge-driven rein- forcement learning pipeline for web agents. Our evaluation spans three key dimensions: (1) testing the synergy of knowledge- and data-driven approaches, (2) benchmarking trained agents across mul- tiple evaluation suites, and (3) analyzing performance when instantiated with different foundation models.
3.1 EXPERIMENTAL SETUP
3.1.1 DATASETS AND BENCHMARKS
We consider three levels of benchmarks. Offline Website Benchmark: an internal benchmark with 100 tasks across 10 offline websites, covering both operational tasks (c.g., adding items to a cart) and information-retrievaal tasks (e.g., extracting product specifications). Tasks are grouped into three dif- ficulty levels: simple (single-step), medium (3-5 steps), and complex (>5 steps). Offline-to-Online Transfer: to measure generalization, we test on three representative online platforms-Amazeee Airbnb, and Booking-with 30 tasks site, evaluating transfer from controlled offline training to real-world execution. Public Benchmarks: we further assess generalization on GUI-Act- Web (Chen al. 2024). OmniAct- Aet-Deskoop (Kapoor et al. [024], and GUI-Odyssey (Lu et al.) 2024). which provide standardized tasks for web and GUI agents.
3.1.2 EVALUATION METRICS
We report three metrics. Task Completion Rate (TCR) measures the percentage successfully com- pleted tasks. Action Accuracy is decomposed into action-type accuracy (Type), grounding accuracy (GR), and success rate (SR). Step Efficiency measures the ratio of executed steps to optimal path length.
3.1.3 BASELINE MODELS
We compare against three representative baselines. QwenVL2.5-3B is an untuned lision-language foundation model (Bai et al. 2025). GPT-4o is OpenAl's multimodal model with strong zero-
shot capability (Achiam et all 2023 GUI-RI-3B is a web agent trained with large-scale human-
annotated data (Luo et al. 2025).

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Table 1: Task generation quality under different config
Table 2: Trajectory data quality
Config
Exe. (%)
Val. (%)
Div.
Cmplx. (%)
Metric
No-Kn.
Kn.
No Knowledge/Data
31.3
42.3
0.31
8.2
SR (%)
42.6
84.3
Data-Only
56.3
68.7
0.52
15.6
Knowledge- -nned
62.5
71.2
0,64
22.3
Steps
15.7
9.8
Knowledge Data
86.3
92.6
0.84
35.7
VD (%)
58.3
89.6
Table 3: Performance on the internal offline website benchmark for operational tasks and informa- tion retrieval, reported by task completion rate (TCR), efficiency, accuracy, and F1 score.
Operational Tasks
Information Retrieval
Model
TCR(%)
Efficiency
Acc.(%
TCR(%)
Score
Acc.(%)
QwenV1.2.5-3B
18.3
0.32
41.2
15.7
0.28
36.4
GPT-4o
26.7
0.41
48.6
22.3
0.35
42.8
GUI-RI-3B
68.2
0.78
85.3
64.6
0.76
81.2
WebFactory-3BB
71.8
0.82
87.6
67.3
0.79
83.4
3.2
EFFECTIVENESS OF KNOWLEDGE AND DATA-DRIVEN APPROACH
3.2.1 IMPACT ON TASK GENERATION QUALITY
We first validate how knowledge and data-driven methods improve task generation quality. For each configuration, we generate tasks and evaluate their executability on actual websites. Table presents the results under different configurations.
The combination of knowledge and data substantially improves task executability from 31.3% to 86.3%. Task validity increases from 42.3% to 92.6%, while knowledge-driven methods leverage website structure information to generate more diverse and complex multi-step interaction tasks, increasing complex task proportion by 4.4x compared to the baseline.
3.2.2 IMPACT ON TRAJECTORY DATA QUALITY
Knowledge-driver methods substantially improve trajectory generation quality. As shown in Ta- ble the success rate nearly doubles (42.6% + 84.3%), while the average number of steps decreases by 38% (15.7 9.8), indicating more efficient task execution. In addition, the proportion of valid data increases from 58.3% to 89.6%, demonstrating a significant improvement data reliability and the overall quality of training trajectories.
3.3 PERFORMANCE ON DIFFERENT BENCHMARKS
3.3.1 INTERNAL OFFLINE WEBSITE BENCHMARK
We further evaluate models on an internal offline website benchmark that covers both operational tasks and information retrieval. As summarized in Table general-purposs vision-language models such as VL2nVL2. and GPT-4o exhibit limited capability, with task completion rates (TCR) be- low 30%. In contrast, models trained with reinforeement learning demonstrate substantially stronger performance. GUI-RI-3B achieves high accuracy across both task types, and our WebFactory-3B model attains comparable results, with slightly higher efficiency and accuracy (e.g.. 71.8% vs. 68.2% TCR and 87.6% vs. 85.3% accuracy on operational tasks). These findings highlight that training solely synthetic data enables WebFactory-3B to reach performance levels par with models trained with large-scale human annotations.
3.3.2 OFFLINE-TO ONLINN TRANSFER
To assess generalization to real-world scenarios, we evaluate models trained offline on three online platforms: Amazon, Airbnb, and Booking. As reported in Table 4 general-purpoe models such as VL2.5-3 and GPT-4o show limited transfer capability, with average task completion rates (TCR) below 40%. In contrast, reinforcement learning- agents achieve markedly better perfor-

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Table 4: Performance on ofline-to-online transfer across Amazon, Airbnb, and Booking, reported by task completion rate (TCR) and accuracy.
Amazon
Airbnb
Booking
Avg.
Model
TCR(%)
Acc.(%)
TCR(%)
Acc.(%)
TCR(%)
Acc.(%)
TCR(%)
QwenVL2.553B
22.3
48.6
18.7
43.2
20.1
45.8
20.4
GPT-40
41.2
68.7
37.8
64.3
39.6
66.2
39.5
GUI-R1-3B
38.6
65.3
35.2
61.7
37.1
63.4
37.0
WebFactory-3B 55.7
79.3
51.2
75.6
53.3
77.4
53.4
Table 5: Generalization on public GUI benchmarks (GUI-Act-Wed and GUI-Odyssey), reported by type accuracy, grounding recall (GR), and success rate (SR). Best results are in bold.
GUI-Act- Web
GUI-Odyssey GR
Setting
Model
Type
GR
SR
Type
SR
GPT-4o
77.1
45,0
41.8
37.5
14.2
5.4
Zero-Shot
Qwen VL2.552.
54.9
63.5
55.6
38.4
27.2
27.2
GUI-RI-3B
89.9
87.4
76.3
54.8
41.5
41.3
RL Fine-Tuning
WebFactory-3B
89.0
82.1
84.2
66.0
48.1
40.9
mance. WebFactory-3BB attains average TCR of 53.4%, representing 162% improvement Vwen51...5-3B (20.4%) and a 44% gain over GUI-R1-3B (37.0%). Furthermore, WebFactory-3B consistently achieves the highest accuracy across all three platforms (79.3% on Amazon, 75.6% on Airbnb, and 77.4% on Booking), underscoring its ability to transfer effectively from synthetic offline training previously unseen online environments.
3.3.3 PUBLIC GUI AGENT BENCHMARKS
Performance on public benchmarks further validates our approach's effectiveness. As shown in Table 5 WebFactory-3B achieves strong generalization across diverse GUI benchmarks. On GUI- Act- Web, obtains the highest success rate (SR) 84.2%, surpassing both GPT-4o (41.8%) and Qwen VL2.532.533B (55.6%). Although GUI-R1-3B yields slightly higher grounding accuracy (GR) on this benchmark (87.4% vs. 82.1%), WebFactory-3B consistently delivers better overall task completion.
On OmniAct-Desktop. WebFactory-33 attains a balanced performance with 85.3% Type accuracy and 73.9% SR, closely matching GUI-R1-3B while significantly outperforming zero-shot foundation models. Most notably, on the challenging GUI-Odyssey benchmark, WebFactory-3B reaches 66.0% Type accuracy, substantially higher than GUI-R1-3B (54.8%), GPT-4o (37.5% ), and QwenVL2. 3B (38.4%). This highlights its robust cross- coos--omaan transfer capability, even though it was trained solely on synthetic data. Overall, these results confirm that WebFactory-333 not only generalizes well but also provides consistent improvements across heterogeneous GUI environments.
3.4 PIPELINE PERFORMANCE WITH DIFFERENT FOUNDATION MODELS
To examine the generalizability of our pipeline and evaluate the LLM embodiment of different foun- dation models, we employ three state-of-the-as LLMs GPT-5, Claude Opus 4.1, and Claude Son- net 4 drive the entire data generation process. Each model functions the architect throughout the entire pipeline: from synthesizing the offline website environments via code generation, for- mulating tasks, and finally collecting interaction trajectories. The resulting agents subsequently evaluated on a diverse suite of benchmarks.
As shown in Figure 3 GPT-5 achieves the strongest overall performance, particularly excelling in Type accuracy while maintaining robust performance across diverse GUI environments. Claude Opus 4.1 performs competitively, yielding slightly lower yet stable results. In contrast, Claude Son- net 4 demonstrates greater variability across benchmarks, indicating less consistent generalization ability.
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AAL byo conparion scnett caRIet
HLL Segp conparion aCOSE DANAME
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Figure 3: Performance comparison of agents trained with data generated by different foundation models across public GUI benchmarks. Results show Type accuracy, Step completion rate, and Grounding accuracy across GUI- Ail-AAt-Werd Web (Chen et al. 2024). GUI-Odyssey (Lu et al. 2024), OmmiActtDesktopp (Kapoor et al. 2024), OmniAct- Web tests, and ScreenSpot categories (desktop- text, desktop-icon, mobile-text, mobile-icon, web-text, web-icon). GPT-5 consistently achieves the highest performance across most metrics, demonstrating superior data generation quality and intel- ligence compression capability.
DISCUSSION
Our agent's superior performance is more than an engineering success; provides compelling evi- dence for our central thesis of intelligence compression. The proposed factory pipeline effectively demonstrates how distill the vast, descriptive knowledge of L.MMs into robust, actionable policies, outperforming even agents trained extensive human data. This success underscores the decisive role of the LLM foundation model itself. Our findings reveal that a model's inherent reasoning and world knowledge directly cap the potential of the final agent, suggesting that the "transferability" and "embodiment potential" are critical, yet underexplored, dimensions for evaluating and selecting foundation models.
These insights motivate a necessary refinement of scaling laws for embodied agents. Analogous to LLM scaling laws, agent's asymptotic performance may be governed not by raw data vol- ume, but by foundation model's intelligence compression efficiency and its inherent capability for embodiment. Our pipeline represents first step in this direction, paving a path toward agents that can rapidly adapt and self-evolve in novel GUI environments by generating their own curricula. While validated here in GUI settings, we believe this paradigm of transforming latent knowledge into grounded action holds strong promise for more complex physical embodied environments.
Future Work. Building on the pipeline's programmability. a promising avenue for future work to leverage WebFactory for targeted capability evolution. Unlike statie datasets, generative infrastructure allows for the systematic probing of specific agent weaknesses-Sure as precise con- tinuous interactions complex logic handling followed foolooeg by the on-demand synthesis of dedicated website environments to address these deficits. This closed-loop mechanism, capable of identifying gaps and algorithmically generating the necessary embodied experiences fill them, transforms the system into a self-correcting engine, further establishing the foundation for truly autonomous and robust agent intelligence.
Limitations. While pipeline demonstrates strong empirical results, identify two primary avenues for future work. First, our work does not include an exhaustive ablation on the impact of different reward mechanisms. A deeper analysis comparing our decomposed reward against sparser or even LLM --Me--eerttedd reward functions could yield further insights into learning dynamics and final policy robustness. Second, Factory pipeline's performance in fundamentally different GUT

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paradigms (e.g., game engines specialized creative software) remains to be systematically vali- dated. Exploring these directions will be crucial for assessing the true generality of our approach.
CONCLUSION
WebFactory demonstrates that nigh-fidelity offline environments, combined with knowledge-drivee task generation and automated RL training, can produce web agents that transfer effectively live websites. By eliminating the brittleness of online experimentation while preserving real-world com- plexity, framework enables reproducible, scalable research. The open-source release of all com- wonents-websites, generators, training pipeline, and evaluation tools-provides a foundation for the community build upon. As the intelligence of foundation LLMs increases and their costs decrease, we expect this fifline-to-oning intelligence compression paradigm to become an increas- ingly practical path capable, general general-purpose purpose web agents.
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Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36:8634488666 8652, 2023.
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A DISCLOSURE OF LLM USE.
We used large language models to assist with language polishing and discovering related work. All technical claims, experiments, and analyses were designed, executed, and verified by the authors.
B RELATED WORK
Web environments and benchmarks. Research on web-agent environments has gradually evolved from simplified DOM-centric tasks to realistic, multi-domain benchmarks. Early con- trolled settings such MiniWoB and Mini Mon+ provided reproducible yet toy-scale interactions for evaluating RL policies (Shi et al. 2017 Liu et al.) 2018). Subsequent efforts increased real- ism: VisualWebArena added multimodal grounding web-page interactions (Koh et al.] 2024), while WebArena introduced high-fidelity. self- hosf-hostable environments covering e-commerce, fo- rums, software development, and content management with integrated tool support (Zhou et al. 2024). WebChoreArena further emphasized long-horizon reasoning and reproducibility by design- ing hundreds of durable, labor-intensive tasks (Miyai et al. 2025). Beyond browser-centric setups, simulation-base environments such as ALFWorld and BabyAl studied language- geggugg-grounded. par- tially observed control problems, offering insights on curriculum learning and generalization (Shrid- har et al. 2020 Chevalier- Boisvert et al. (2018). Unlike live-web web evaluations, which are hindered by CAPTCHAs, layout drift, and network nondeterminism, WebFactory adopts a versioned, fully of- fline, pre-authenticatee design with deterministic rendering and explicit knowledge/data snapshots, enabling verifiable answers and replayable trujectories at scale.
Frameworks and datasets. parallel line work has focused on standardizing interfaces and expanding coverage. BrowserGyy unifies APIs across multiple environments (MiniWob) Visu- alWebArena, WebArena), facilitating consistent comparison of agents (Chezelles et al., (024)) Web aggregates thousands of human-annotatte tasks across diverse websites, emphasizing breadth and realistic natural-language instructions (Deng et al. (023). Other benchmarks extend to OS- and GUI-level control, such as Windows Agent Arena, OSWorld, Android-in-the-wiill. and GUI-Odyssey, which target distinct substrates and I/O stacks beyond the browser (Bonatti et al., 2024 Abhyankar et al. [2025] Rawles et al. 20233 Lu et al. (2024). In contrast, WebFactory focuses on browser-native interaction under complete controllability, while remaining compatible with com- evaluation interfaces for cross-benchmark comparison.
Training paradigms for web agents. Recent work has explored both supervised sequence mod- eling and RL-based methods tailored to long-horizon web interaction. Decision Transformer applies return-conditooned sequence modeling to agent trajectories (Chen et al.) 2021): conservative offline RL enhances safety when learning from static datasets (Kostrikov et al. 2021): and preference- or feedback-driven optimization aligns policies with human intent (Christiano al. 2017 Ouyang
et al. 2022). Web-specific innovations include curricula derived from agent failure modes and outcome-supervised reward modeling (Qi et al. 2024). success-driven rollouts (Wei et al. 2025), reusable skill abstractions (Zheng et al. 2025). and hierarchical formulations for decomposing complex browsing workflows into subgoals (Furuta et al. (023). WebFactory complements these paradigms by offering scalable pipeline where synthetic trajectories, unified action spaces, and decomposed rewards can be directly applied train robust web agents.
Reasoning, exploration, and data collection. Reasoning scaffolds such as ReAct, Voyager. Re- flexion, and Trec-of-Thougee enhance planning, celf-correction, and exploration (Yao et al. 2023b Wang al., 2023, [hhinn et al.) 2023] Yao et al., (003a)). On the data side, Go-Browse uses graph-guided exploration to diversify trajectories (Gandhi & Neubig, 2025); WebVoyager retro- spectively synthesizes demonstrations from failures without human annotations (He et al.) 2024);
and AgentOccam streamlines observation-action design to align with LLM reasoning (Yagg et al. 2024]. Curriculum based difficulty further adapts training to agent errors (Qi et al. 2024). Web- Factory provides reproducible substrate- explicit tasks, normalized answers, and deterministic replays-to to evaluate reasoning/cxplooation methods and generate large, high-signal offline datasets without costly online rollouts.
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Live-web automatic task/trajectory generation. Synatra converts human-orienter tutorials and indirect instructions into synthetic, executable demonstrations for web agents, enabling large-scale supervision without manual trajectory annotation (Ou et al. 2024). Harnessing Webpage UIs builds a large text-rich visual understanding dataset from real webpages, exploiting UI structure as a mul- timodal supervision signal (Liu et al.) 2024). PAE PaaposerrAgeet--vvalatoooo
introduces pro- poser-agent-evaluator loop that autonomously proposes web tasks, executes them, and filters tra- jectories to discover reusable skills for foundation-model agents (Zhou et al. 2025). NNetNav leverages unsupervised interaction with live websites together with hindsight relabelling to con- struct browser-agent training data directly from in-the-wild behavior (Murty al.) 2024). InSTA pushes this line to internet scale, generating and judging tasks and trajectories across a large number of live websites with L.LM-based agents and evaluators (Trabucco al. 2025). These systems col- lectively excel in scale and real-world diversity on the live web. In contrast, Web operates on a fully offline, versioned web suite with deterministic rendering, explicit knowledge/data snapshots, verifiable optimal paths, and replayable trajectories, prioritizing determinism, precise reward spec- ification, and reproducibility, and thus providing a complementary substrate to live-web pipelines rather than replacement.
knwwledge-to-trajeede Weeuk/intireet-xxwwwlde------ shopping environment on real e-commerce sites, using product metadata and textual descriptions to supervise grounded navigation and decision making (Yao et al. 2022). Synatra converts human- oriented web tutorials and other indirect instructions into large-scale executable demonstrations, providing high-coverage synthetic supervision for web agents (Ou et al. 2024). AgentTrek synthe- sizes trajectories by guiding replay with web tutorials as high-level instruction sequences, tightening the link between weak textual knowledge and concrete action sequences (Xu et al. 2024). Explorer scales exploration-diie
Weak/indirect-
pipelines. WebShop formulates a goal-conditioned
web trajectory synthesis across many real webpages, using multimodal exploration signals to expand demonstration coverage for multimodal web agents (Pahuja et al, 2025). These pipelines demonstrate how weak or indirect web knowledge can be systematically transformed into training data the live web. Fectact instead operates in fully controllable offline suite with deterministic rendering, structured knowledge snapshots, and verifiable optimal paths, offering reproducible substrate that is complementary to these weak-supervision pipelines.
C BUILDING THE OFFLINE WEBSITE SUITE
C.I DESIGN GOALS
We target high-fidelity yet fully controllable suite that (i) boots to pre-authenticated, seeded ses- sions, (ii) exposes ground-trute -tuth knowledge and data for generation and evaluation, (iii) disables anti-automation friction (CAPTCHA, bot detection), and (iv) is versioned and reproducible.
C.2 LLM-DRIVEN BUILD PROCESS
We have developed a method for scalable, high-fidelity offline website generation using LLMs, which we plan to open source to facilitate reproducibility and community extension. The construc- tion of each offline website is fully automated: the "site recipe" is executed by LLM-driven coding extensible engine where LLMSs function embodied architects,
agents. WebFactory acts enabling scalable generation of high-fidelity web environments.
Our automated build process follows uniform site recipe across domains (e-commerce, travel, etc.):
Scaffold & theming. Initialize a Next.js/eeact monorepo with shared UI kit (forms, tables, modal, hover menus, and-dro... Provide mobile/desktop breakpoints to produce realistic layout variety.
2. Data layer materialization. For each site family, export versioned static snapshot (Data.js/JSON) with deterministic seeds. Schema includes entities (e.g., Product, Hotel, Flight, Message), relations, and canonical views (lissdetail/cart/hhekkku)))
3. Navigation graph & flows. Encode page graph and canonical flows (e.g., browse + detail + cart) in a machine-readable knowledge. json. Each node stores visible affordances and element locators.
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4. Anti-bot off-switch. Gate any bot-detection middleware by build flag: fall back to challenge in offline mode.
Benchmark export. For each version v, release (i) site bundle, (ii) knowledge. json, (iii)
Data. json, and (iv) scripted evaluators.
Table Overview of offline website families used our benchmark, including their domains and representative core functionalities.
Name
Domain
Core Functionality
Shopping
E-commerce marketplace
Marketplace with scarch, multi-facct filters, cart/wishlist, reviews, multi-step checkout.
Mealdash
Food delivery
Restourant discovery. dietary filters, with quantity/notes, schedul- ing. order tracking.
Hotels
Hotel booking
Location//dategueee search, amenity filters, mom types, price tiers, availability reservation.
Flights
Flight search & booking
One- way/rturr/muuti-cit flexible dates, carriers/cabin filters, fare comparison, scat selection.
Careerlink
Professional networking jobs
Job seurch by skills/company, profiles, applications, resume manage- ment, insights.
Carrental
Car rental
Pickup/dropoff, vehicle class, insurance add-ons, driver requirements, hooking changes.
Masterticket
Event ticketing
Artist/venuc scarch, categorics, date filters, map, ticket types, fees, checkout.
Staybub
Short-term rentals
Rentals by location/dates/guests, umenity filters, calendars, pricing. booking tlows. Folders, compose/replyfoorwat attachments, rules, search, threads, conlacts.
Fmail
Email client
Companycheck
Company data intelligence
Company profiles, filings, exccutives, relationship graph, compliance duc diligence.
D TASK SYNTHESIS DETAILS
D.1 DUAL- GENERATION
(1) deiplate-driven Define modular spaces for search, filtering, sorting, cart/checkout, form com- pletion, multi-form workflows, and cross-page navigation. Instantiate by sampling from versioned Data.* * with constraints (e.g., "price < $200", "rating 2 4"" while respecting site schemas.
(2) LLM-assisted Feed compact knowledge slices-navigenn navigation graph, page affordances, canonical flows and sampled data skims to LLM to propose task paths beyond the template envelope (long-horizon operations and compositional IR).
D.2 VALIDATORS & EXECUTABILITY
All candidates pass:
Schema conformance: fields and operators exist in Data.json,
Visibility check: referenced elements/records are reachable and visible given viewport and filters (uses layout probes).
Path feasibility: dry-run along the known navigation path (knowledge.json failure short- circuits.
Answerability (IR): canonical answers exist in the data layer with unique normalized target.
D.3 DIFFICULTY CONTROL & CURRICULUM
We sample along three axes: data complexity (catalog/graph density), UI complexity (multi-level nav, drag-and-drop, hover), and workflow depth (lookup multi-step execution). Curricula ramp
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Algorithm Knowledge-driven Task Factory
1: for site in sites do
2: K + mad__knwleddg())) D f (atad_
3:
for spec in templates U [1l_proompe do cands + instantiatc(spec, D, difficulty)
4:
5:
for t in cands do schema_ok(t, D) && visible(t, K) && fath_feasible(t, K)) then attach lacgso__dhhhh,,,___e- K); emit
6:
7:
8: end if
9: end for
10: end for
end for
(1) start URLs, (ii) number of required filters, (iii) cross-page hops, (iv) time/step budget. Each emitted task is stored with difficulty tag and gold path.
E TRAJECTORY GENERATION DETAILS
E.1 EXECUTOR & INSTRUMENTATION
We execute the pre-validated task T with strong executor (OpenAl computer-use-previee inside the offline suite. Each step logs:
page ID, viewport hash, DOM key-node set;
action tuple a, {atet o, a.oint a. aeentt and matched locator;
state diff summary (element attributes, cart contents, form values).
Screenshots and structured traces are stored in Parquet; per-episode metadata carries seed, site ver- sion, and curriculum tier.
E.2 FILTERING & DETERMINISM
remove low-quality traces via:
Deterministic replay: re-rum with the same seed; reject if hashes (viewport, key-node set, cart snapshot) mismatch.
2. Key-node coverage: ensure required nodes along the gold path are visited in the right order.
3. IR validation: compute normalized F, against canonical answer; drop if below threshold.
Accepted trajectories populate the replay buffer B with tuples (st, a,, R2, s(+1).
F TRAJECTORY DATASET STATISTICS & DISTRIBUTIONS
To better understand the dynamics of user interactions within the trajectory dataset, analyze both the overall action distribution and the transition patterns between different actions.
F.1 ACTION DISTRIBUTION
Figure[pressent the distribution of ground-truth actions. The dataset is dominated by click actions, which account for nearly half of all recorded interactions (47.89 1.... This is followed by wait (24.1% and scroll (20.9%), reflecting common patterns in typical graphical user interface (GUI) usage. Less frequent actions include type (5.3%), keypress (1.8%), and double_clies (0.2%). These results suggest that the dataset heavily skewed towards basic navigational primitives (click, wait, and scroll), which together comprise over 90% of the interactions.
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Ground Truth Actions Distibution
2055
Figure 4: Ground truth action distribution in the dataset.
F.2 ACTION TRANSITION DYNAMICS
To capture sequential dependencies, we compute the transition frequency between all pairs of actions (Figure 5}. The heatmap reveals several key patterns:
click frequently transitions back itself (812 times), and is also followed by wait (551) and scroll (191). This indicates that clicking is often interleaved with periods waiting subsequent navigation.
scroll transitions strongly to itself (419) and also to click (289), reflecting the natural alternation between scrolling content and selecting items.
wait is another central action, often followed by click (480) and self-repetition (241), which captures idle or delay states before further activity.
Rare transitions occur for double _ccckkk and keypress, consistent with their overall low frequency.
Together, these findings highlight that the dataset is structured around small number of dominant action primitives, with strong self-loops and predictable sequential dynamics. Such patterns valuable for modeling purposes, as they suggest that predictive models may benefit from emphasiz- ing high-frequency transitions while carefully handling the long-tail actions.
G
RL TRAINING DETAILS
G. ACTION SPACE WWEB-SPECIHI
We operate in a structured space:
a, = {att point of (oxt),
att E fclick, double type, scroll, keypress, keypkk, drag, get_final_answer]
A concise specification is provided in Table 7
G.2 REWARD
Per-step reward:
Rg aR, + Raccuacy,
(4)
where Ry enforces structured outputs (valid JSON, tags, type constraints) and R Raccuracy is action- specific:
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Action Transition Healmap
012
doubrrudick
000
From Aatton
knypress
Count
scroli
-900
700
dedile Clac
Lenooeea
enco
Figure 5: Action transition heatmap showing transition counts between actions.
Table 7: Detailed specification of the web agent action space, listing each supported action together with its required point parameters (coordinates) and text parameters.
Action
Point Parameter
Text Parameter
click
[r. y]
double_click
[x, y]
type
[ 100, -100
input text (required)
scroll
|- 100, 100]
UP/DOWN
keypress
[ 100, 100
key name (e.g., ENTER)
drag
[x1 31), [22 y2]
UP/DOWN
get_final_answer
[ 100, 100
final answer text
Click family (cLick/double_click)) inside-target check distance tolerance (140px) to ele- ment center.
Text family (type/keypress). token-level F, with case/punctuation normalization; single-token special-cased.
Scroll/rrag: exact direction match; drag validates source source-target target coordinates.
IR answer: final answer scored by normalized F, against canonical target.
G.3 TRAINING ALGORITHM (GRPO/PPO)
We extend GUI-RI with an IR-aware head and reward. GRPO is used for group-normalized advan- tages multi-sample rollouts.
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Algorithm 2 GRPO for Web Agents
1: for episode = 1 to E do
2: Sample task batch {pi}
3: for each do
4:
Generate n trajectories; compute R
5: [4; - 1--mmaan(Ae R,,,), f (4,x) A., < (Ra, - (4)/(a; +
6:
7:
end for
8:
Update with PPO loss and KL penalty
9: if mod then
10: save checkpoint
end
12:
H IMPLEMENTATION DETAILS
H.1 TRAINING INFRASTRUCTURE
Model. Owwn2..5-VL-3B. vision encoder initially frozen; max screenshot 1258 x 1258. Distributed training.
Actors: 4 GPUs with FSDP; rollouts: 1 GPU via vLLM.
Global batch 64, micro-batch 4; optimizer states CPU offloaded.
Optimization. AdamW, Ir x 10-6, wd 0.01, grad clip 1.0, fixed KL 0.01.
H.2
WEB AGENT DATA PROCESSING
Parquet traces contain (i) screenshots + action history, (ii) task instruction, (iii) ground-truth actions with bboxes, (iv) task-type flags.
H.3 REWARD COMPUTATION FOR WEB TASKS
See Sec. G for per-action rules; format reward enforces valid JSON, required <think>/<answer> tags, numeric types, and conditional fields.
Table 8: Hyperparameters used for web agent RL training.
Parameter
Value
Episodes F
15
Rollout temperature
1.0
Responses per prompt n
Global / micro batch
64 /
Max prompt / response tokens
2048 1024
Image resolution
1258 1258
PPO clip
0.2
Discount ?
1.0
(format) / (accuracy)
0.2
KL. penalty
0.01
Learning rate
1 10-6
GPUs
4 x V100/A100
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Published as a conference paper at ICLR 2026
I OFFLINE-TO-ONLING TRANSFER EVALUATION DETAILS
In this section, we provide comprehensive specification of the experimental protocol used for the Offlnneto-Online Transfer evaluation (Table 4 in the main text). To ensure rigorous validation of agent capabilltiess in live, dynamic environments, we constructed a custom benchmark of 90 live tasks across Amazon, Airbnb, and Booking.
1.1 METHODOLOGY FOR UNBIASED TASK CONSTRUCTION
To prevent selection bias and ensure the benchmark accurately reflects real-world usage, adhered to a "User-First, Model-Agnostic design protocol. Tasks were defined based on necessary user workflows prior to any agent evaluation.
Funnel-Basee Coverage: We strictly stratified tasks by user conversion stages to ensure full spectrum coverage:
Discovery:
Ambiguous search queries requiring exploration.
Refinement: Complex filtering involving price ranges, brands, and amenity straints.
Action: State-changing operations such adding items cart or selecting specific dates.
Complexity Alignment: deliberately excluded trivial single-step tasks. All tasks align with "Medium" (3-5 steps) and "High" (>5 steps) complexity tiers to force long-horizon reasoning.
Cross-Domain Mapping: Online platforms were selected to strictly correspond to our offline training domains to evaluate transfer capability: Amazon Shopping, Airbnb StayBnB, and Booking - Hotels.
1.2 EXECUTABILITY AND STABILITY VERIFICATION
Live websites are subject to frequent changes, A/B testing, and inventory fluctuations. rule out failures caused by external factors (e.g., out-of-stock items or UI updates), all tasks are manually verified for feasibility within 24 hours prior to agent evaluation. This ensures that reported failures due to agent capability rather than environmental errors.
1.3 STATISTICAL SIGNIFICANCE ANALYSIS
Given the sample size of N 90 (30 tasks per platform), we calculated the 95% Confidence Intervals (CI) for the success rates using the Wilson Score Interval method, which is robust for smaller sample sizes.
Baseline (QwenVL2.5-3B): 20.4% [95% CI: 13.0% 30.0%
WebFactory-3B (Ours): 53.4% [95% CI: 43.0% 63.5%)
Notably, there is no overlap between the confidence intervals. The lower bound of our method (43.0%) is substantially higher than the upper bound of the baseline (30.0%). This statistical analysis confirms that the 162% relative improvement reported in the main text is robust and significant, rather than result of sampling noise.
1.4 REPRESENTATIVE TASK SPECIFICATIONS
Table 9 provides representative examples of the tasks used in the evaluation, detailing the required interactions and complexity constraints.
Conclusion Rigor. The tasks detailed above involve Modal Interactions, Calendar Pickers, and Multi-step Filtering complex UI patterns that standard multimodal models (c.g., QwenVL2.5,, 20.4% SR) struggle handle zero-shot. The 53.4% success rate of WebFactory-3B. supported by
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Published as a conference paper at ICLR 2026
Table Representative tasks from the Offline-to-Onlln Transfer Benchmark. Tasks are designed to test specific interaction capabilities including precise search, constraint filtering, and complex state changes.
Platform
Category
Design & Complexity Con- straints
Representative Instruction
Precise
Precise keyword matching: navigate to detail page to verify specs.
Req:
"Search 'Sony WH- 1000XM5 headphones', click on the black version. and tell me the current price."
Search
Amazon
UT: Search har, Click.
B:
Constraint
Req:
Apply multiple compound filters (price, brand, Prime) before clicking result.
"Find a 'Gaming Monitor' under $300 from 'ASUS' with "144Hz" refresh rate. Add the first result to the cart."
Filtering
UI: Sidebar filters.
C: Cart Flow
Req: State changes; multi-page navigation.
"Add a 'Logitech MX Master 3s' to your cart, then the cart and change the quantity to 2."
UI: Add-to-cart, verify cart.
A:
Date/Loc
Req:
Complex calendar interac- tion; location input.
"Search for stay in 'Ky- oto, Japan' for '2 adults' from 'September 10' to 'SSettember 15'."
Search
Airbnb
UI: Date-picker, search field.
B: Amenity tering
Req: Open modal window; scroll to find/check specific boxes. Ul: Modal popups, checkboxes.
"Find a home 'Paris' that has 'Wifi', 'Kitchen', and "Washing Machine' Open the detail page of the highest-rated listing."
C: Detail
Ex-
Req: Parse unstructured long- text descriptions.
"Go to the first listing for "Cabin in Lake Tahoc' and verify Pets allowed in the house rules."
traction
UI: Text parsing, scrolling.
A:
Multi-
Req: Handle location, dates, room/guest config simultane- ously.
"Search for hotel in 'New York' adults. child" for the weekend of 'September 14th'?
Criteria
Booking
Ul: Complex form filling.
B: Sort Seleet
Req: Operate sorting dropdowns; select under constraints.
"Sort hotels in 'London' by 'Top Reviewed' and click on the ho- tel with the highest score under 1200/night."
UI: Dropdowns, list parsing.
C: Room Config
Req: Navigate to detail page; ex- tensive scroll; specific selection. UI: Deep navigation.
"Search "Hilton Tokyo', scroll to available rooms, and select a 'King Room' with 'Breakfast In- cluded"."
non-overlapping confidence intervals, provides strong evidence that our "Intelligence Compression" paradigm successfully transfers generalizable logic from controlled offline data to unseen, noisy online environments.
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Reviews:
[{"review_id": "Sq5W0FX7mY", "reviewer": "Reviewer_ZY71", "summary": "This paper introduces WebFactory, a pipeline for automating the training of GUI-based web agents. The core idea is to use a high-fidelity, offline environment to synthesize tasks, generate trajectories with a strong LLM executor, and train an agent via reinforcement learning with a decomposed reward function. The authors claim this approach achieves superior data efficiency and generalization, with an agent trained on only 10 synthetic websites performing competitively against agents trained on larger sets of human-annotated data. The work also introduces the concept of \"LLM embodiment\" as a new axis for model evaluation.", "questions": "Please refer to the weakness part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "I believe this paper propose some perspectives beyond a method to train the web agent. For example, paradigm shift from fine-tuning LLM to treating the LLM as embodiment. In this way, the LLM could be treated beyond the traditional SFT or RL paradigm. \n\nBesides, there are some open-source contributions. Authors are releasing the entire toolchain, including the 10 high-fidelity offline environments, task generators, and training pipeline. This is a engineering contribution that enables reproducible research and provides a high-quality, non-chaotic testbed for the community, distinct from the \"noisy live web\".", "weaknesses": "There is a contradiction in motivation and scalability. The introduction opens by attacking the reliance on \"costly, scarce human-crafted data and environments\" and explicitly calls out the \"painstaking, manual synthesis of high-fidelity environments\" as a bottleneck that \"can itself consume weeks of expert effort\". However, the entire WebFactory method is predicated on exactly this: a suite of 10 high-fidelity offline websites, which Appendix C reveals were meticulously built by the authors as a \"Next.js/React monorepo\". The \"fully automated\"  pipeline only functions after this massive, non-scalable, human-expert-driven environment creation step is complete. This is a glaring contradiction. The paper does not solve the environment synthesis bottleneck; it leverages it.\n\nIn this way, the claim of a \"fully automated\" pipeline is misleading. The automation only applies to data generation within the pre-built, sanitized, and manually-instrumented environment. This is a far from a system that can be pointed at website.\n\nWhile the agent outperforms the baseline on the online transfer task (Tab. 4), its absolute performance is still low. An average TCR of 53.4% means the agent fails its task nearly half the time on real-world sites like Amazon and Airbnb. This highlights a major sim-to-real gap that the 10 pristine, static, React-based environments  fail to capture from the chaotic, non-deterministic, and constantly changing live web.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HI3FwFCWuO", "reviewer": "Reviewer_7HF2", "summary": "The paper contributes a data generation pipeline for end-to-end training of GUI computer use agents that (1) constructs synthetic websites with diverse layouts and control interfaces, (2) populates these websites with synthetic listings for products / other materials, and (3) employs knowledge of the raw website product / listing data + the navigation structured between pages to efficiently generate verifiable web navigation tasks with a known optimal completion paths. \n\n\nThe proposed pipeline is employed as an environment for training GUI agents derived from GUI-R1, and results show that RL on synthetic websites and tasks from the proposed pipeline can be as effective as training on human data (ie, human demonstrations, and human-written tasks).", "questions": "In Table 2, are the statistics reported for the same set of tasks, or are the task being varied (ie, statistics of trajectories for tasks generated with the Knowledge config vs without the Knowledge config)?\n\n\nLine 346 reports that the decrease in steps is due to more efficient task execution, but if the tasks are being varied in this experiment, this could also be explained by a shift in the task distribution.\n\n\nWhich set of tasks are used in Table 4, and how are these selected? Are these from a standard dataset, or generated by the proposed method (important to know to ground the results here)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "## Originality:\n\nThere has been an increasing focus on solving the data problem for computer use agents in recent literature, and this is a problem of major importance in my opinion. To my knowledge, prop works have engaged largely with existing websites on the internet in the construction of trajectories and tasks, existing human-created benchmarks like WebArena. The problem statement of generating synthetic websites is original, and potentially a valuable tool for targeting the data distribution to models’ weaknesses.\n\n\nA large focus of the paper’s motivation is on developing a scalable pipeline for task / environment  generation, but the discussion of current efforts could be more thorough. For example, there have been significant efforts this past year in creating computer use tasks with verifiable evaluation criteria on live websites that are not mentioned in the discussion, and must be considered when evaluating originality, such as:\n\n* [1] Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale, Ou et al. 2025\n* [2] Harnessing Webpage UIs for Text-Rich Visual Understanding, Liu et al. 2022\n* [3] Proposer-Agent-Evaluator(PAE): Autonomous Skill Discovery For Foundation Model Internet Agents, Zhou et al. 2024\n* [4] NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild, Murty et al. 2025\n* [5] InSTA: Towards Internet-Scale Training For Agents, Trabucco et al. 2025\n\nThe paper would be improved by incorporating these citations into its discussion where relevant.\n\nTo my knowledge, generating synthetic websites is an original contribution, and has the potential to allow the pipeline to target weaknesses in the learner, which is a valuable capability not explored in this work.\n\n## Quality:\n\n\nOverall the paper is organized logically, and most important experimental questions are answered by the end. Relevant datasets are selected from the literature, and several baseline models are included that are relevant.\n\n## Clarity:\n\n\nThe paper is generally written clearly, though some minor details could be improved, mentioned in the next section.\n\n## Significance:\n\n\nAs discussed in my review of the paper’s originality, the notion of distilling language intelligence into agentic capabilities is not itself a new idea in the realm of web agent research [1-5]. In addition, several previous research methods have been scaled to extract tasks / trajectories from millions of websites [1,2,5] and can be considered scalable approaches for this reason. Therefore, the significance of this paper derives from the capability of the proposed system to generate specific targeted data with rich layouts, specific verifiable tasks with known optimal completion paths, and the ability to be deterministic (important for benchmarks).", "weaknesses": "While the reported numbers are impressive---an improvement of 162% over the Qwen VL baseline in Table 4 is large and a good validation of the approach---it is not clear in this experiment how tasks are created in this evaluation, and the number of websites + tasks is low (90 according to the experimental details). This number is low enough that I would be interested to see error bars (standard deviation, and 95% confidence interval) reported for this experiment in order to improve my confidence that the results are indeed this much better. \n\nIn addition, if the tasks employed in Table 4 are not from a standard benchmark, but are instead created by the authors for the purposes of evaluating their agent, the methodology employed for task creation should be included in the paper, with a discussion of the steps taken to ensure that there is no bias introduced by potentially designing tasks in a way that favor their approach, which would make the results less trustworthy.\n\n\nFinally, the experiments miss an interesting capability of the proposed method: the ability to probe the weaknesses of current GUI agents and generate websites that target those weaknesses. Since this is a fairly sophisticated suggestion that is unlikely to be feasible during peer review, this weakness is mainly as a suggestion for improving the paper, and not one that impacts my score.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FasSG0Flv4", "reviewer": "Reviewer_iASv", "summary": "The paper introduces WebFactory, an automated, closed-loop pipeline for converting an LLM’s largely descriptive web knowledge into executable GUI/web-agent policies. The key design choice is to avoid the instability of the live web by operating in a fully controlled offline clone of real sites. The system (1) programmatically generates tasks from site knowledge, (2) uses a strong LLM to collect successful trajectories, and (3) trains an RL agent over a unified action space with decomposed rewards. The training is done on synthetic trajectories from 10 sites, and the resulting agent matches or has gains over agents trained on similarly sized human data, and shows non-trivial transfer to real platforms. The authors interpret this as a form of intelligence compression and propose LLM embodiment as a complementary axis for comparing foundation models.", "questions": "See the weakness section for my main questions.\n\nIn the context of automatic pipelines that turn weak/indirect web knowledge into trajectories, run training, and test on real env, there are some references that I think should be discussed as well:\n- WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents\n- Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale\n- AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials\n- Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper tackles an important problem of grounding agents to real website with cheap and controllable data\n- The idea is simple and easy to build upon\n- The empirical result are strong and the gains are significant", "weaknesses": "The \"compression\" language seems odd to me. It makes sense to claim that training the model on data generated in a sim/controllable environment enables the model to generalize to a more realistic environment, but this compression framing makes it more confusing than necessary. There's already some early work related to sim2real that discuss how to use envs that are cheap and controllable to transfer to real env. I can see in some way of defining \"compression\" that stresses the knowledge part, but in that case more ablation/analysis should be included. E.g., Table 1 shows a breakdown w.r.t. the generation quality (executability, validity, etc) but not final RL agent performance.\n\nThe other main weakness is the lack of ablations. Out of the 10 websites which contributes most to the final performance? How does the performance curve look like as you add from fewer to more websites? How fast does the performance plateau (i.e., what's the diminishing return of adding envs)?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)