WebFactory: Automated Compression of Foundational Language Intelligence into Grounded Web Agents

• URL: http://arxiv.org/abs/2603.05044v1
• Date: Thu, 05 Mar 2026 10:51:34 GMT
• Title: 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,
• Abstract summary: We introduce WebFactory, a novel, fully automated closed-loop reinforcement learning pipeline for GUI agents.<n>Our agent demonstrates exceptional data efficiency and generalization.<n>This work presents a scalable and cost-effective paradigm for transforming passive internet knowledge into active, grounded intelligence.
• Score: 20.85611634311147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, and 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 the same amount of human-annotated data from a much larger set of environments. This superior performance is consistent across our internal offline and online transfer 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.

Related papers

• OpAgent: Operator Agent for Web Navigation [23.928869500029432]
  We develop an online interaction environment and fine-tune the Vision-Language Model (VLM) using a specialized RL pipeline.<n>We introduce a Hybrid Reward Mechanism that combines a ground-truth-agnostic WebJudge for holistic outcome assessment and a Rule-based Decision Tree (RDT) for progress reward.<n> Notably, our RL-enhanced model achieves a 38.1% success rate (pass@5) on WebArena, outperforming all existing monolithic baselines.
  arXiv  Detail & Related papers  (2026-02-14T02:33:55Z)
• Scaling Agent Learning via Experience Synthesis [100.42712232390532]
  Reinforcement learning can empower autonomous agents by enabling self-improvement through interaction.<n>But its practical adoption remains challenging due to costly rollouts, limited task diversity, unreliable reward signals, and infrastructure complexity.<n>We introduce DreamGym, the first unified framework designed to synthesize diverse experiences with scalability in mind.
  arXiv  Detail & Related papers  (2025-11-05T18:58:48Z)
• WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning [73.91893534088798]
  WebSailor is a complete post-training methodology designed to instill this crucial capability.<n>Our approach involves generating novel, high-uncertainty tasks through structured sampling and information obfuscation.<n>WebSailor significantly outperforms all open-source agents in complex information-seeking tasks.
  arXiv  Detail & Related papers  (2025-09-16T17:57:03Z)
• SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents [93.26456498576181]
  This paper focuses on the development of native Autonomous Single-Agent models for Deep Research.<n>Our best variant SFR-DR-20B achieves up to 28.7% on Humanity's Last Exam benchmark.
  arXiv  Detail & Related papers  (2025-09-08T02:07:09Z)
• WebSynthesis: World-Model-Guided MCTS for Efficient WebUI-Trajectory Synthesis [34.998277998052444]
  We propose WebSynthesis, a novel framework for trajectory synthesis and training.<n>We show that an agent trained using WebSynthesis on a small-scale synthetic dataset achieves performance comparable to or even surpassing that of models trained on large-scale real-world data.
  arXiv  Detail & Related papers  (2025-07-06T12:31:10Z)
• WebSailor: Navigating Super-human Reasoning for Web Agent [72.5231321118689]
  WebSailor is a complete post-training methodology designed to instill this crucial capability.<n>Our approach involves generating novel, high-uncertainty tasks through structured sampling and information obfuscation.<n>WebSailor significantly outperforms all opensource agents in complex information-seeking tasks.
  arXiv  Detail & Related papers  (2025-07-03T12:59:07Z)
• WebEvolver: Enhancing Web Agent Self-Improvement with Coevolving World Model [55.276852838877346]
  Self-evolving agents are trained on trajectories sampled autonomously based on their own policies.<n>We propose a novel framework that introduces a co-evolving World Model LLM.<n>This world model predicts the next observation based on the current observation and action within the web environment.
  arXiv  Detail & Related papers  (2025-04-23T02:54:31Z)
• Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments [33.83610929282721]
  Learn-by-interact is a data-centric framework to adapt large language models (LLMs) to any given environments without human annotations.<n>We assess the quality of our synthetic data by using them in both training-based scenarios and training-free in-context learning (ICL)<n>Experiments on SWE-bench, WebArena, OSWorld and Spider2-V spanning across realistic coding, web, and desktop environments show the effectiveness of Learn-by-interact.
  arXiv  Detail & Related papers  (2025-01-18T22:34:41Z)
• AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials [53.376263056033046]
  Existing approaches rely on expensive human annotation, making them unsustainable at scale.<n>We propose AgentTrek, a scalable data synthesis pipeline that generates web agent trajectories by leveraging publicly available tutorials.<n>Our fully automated approach significantly reduces data collection costs, achieving a cost of just $0.55 per high-quality trajectory without human annotators.
  arXiv  Detail & Related papers  (2024-12-12T18:59:27Z)
• AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents [52.13695464678006]
  This study enhances an LLM-based web agent by simply refining its observation and action space.<n>AgentOccam surpasses the previous state-of-the-art and concurrent work by 9.8 (+29.4%) and 5.9 (+15.8%) absolute points respectively.
  arXiv  Detail & Related papers  (2024-10-17T17:50:38Z)
• Large Language Models Can Self-Improve At Web Agent Tasks [37.17001438055515]
  Large language models (LLMs) have recently demonstrated some capability to navigate novel environments as agents in a zero-shot or few-shot fashion. We explore the extent to which LLMs can self-improve their performance as agents in long-horizon tasks in a complex environment using the WebArena benchmark. We achieve a 31% improvement in task completion rate over the base model on the WebArena benchmark through a self-improvement procedure.
  arXiv  Detail & Related papers  (2024-05-30T17:52:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.