Adapting Web Agents with Synthetic Supervision

• URL: http://arxiv.org/abs/2511.06101v1
• Date: Sat, 08 Nov 2025 18:45:33 GMT
• Title: Adapting Web Agents with Synthetic Supervision
• Authors: Zhaoyang Wang, Yiming Liang, Xuchao Zhang, Qianhui Wu, Siwei Han, Anson Bastos, Rujia Wang, Chetan Bansal, Baolin Peng, Jianfeng Gao, Saravan Rajmohan, Huaxiu Yao,
• Abstract summary: Web agents struggle to adapt to new websites due to the scarcity of environment specific tasks and demonstrations.<n>Recent works have explored synthetic data generation to address this challenge.<n>We propose SynthAgent, a fully synthetic supervision framework.
• Score: 80.89365133130558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Web agents struggle to adapt to new websites due to the scarcity of environment specific tasks and demonstrations. Recent works have explored synthetic data generation to address this challenge, however, they suffer from data quality issues where synthesized tasks contain hallucinations that cannot be executed, and collected trajectories are noisy with redundant or misaligned actions. In this paper, we propose SynthAgent, a fully synthetic supervision framework that aims at improving synthetic data quality via dual refinement of both tasks and trajectories. Our approach begins by synthesizing diverse tasks through categorized exploration of web elements, ensuring efficient coverage of the target environment. During trajectory collection, we refine tasks when conflicts with actual observations are detected, mitigating hallucinations while maintaining task consistency. After collection, we conduct trajectory refinement with a global context to mitigate potential noise or misalignments. Finally, we fine-tune open-source web agents on the refined synthetic data to adapt them to the target environment. Experimental results demonstrate that SynthAgent outperforms existing synthetic data methods, validating the importance of high-quality synthetic supervision. The code will be publicly available at https://github.com/aiming-lab/SynthAgent.

Related papers

• EvoSyn: Generalizable Evolutionary Data Synthesis for Verifiable Learning [63.03672166010434]
  We introduce an evolutionary, task-agnostic, strategy-guided, executably-checkable data synthesis framework.<n>It jointly synthesizes problems, diverse candidate solutions, and verification artifacts.<n>It iteratively discovers strategies via a consistency-based evaluator that enforces agreement between human-annotated and strategy-induced checks.
  arXiv  Detail & Related papers  (2025-10-20T11:56:35Z)
• Synthesizing Agentic Data for Web Agents with Progressive Difficulty Enhancement Mechanisms [81.90219895125178]
  Web-based 'deep research' agents aim to solve complex question - answering tasks through long-horizon interactions with online tools.<n>These tasks remain challenging, as the underlying language models are often not optimized for long-horizon reasoning.<n>We introduce a two-pronged data synthesis pipeline that generates question - answer pairs by progressively increasing complexity.
  arXiv  Detail & Related papers  (2025-10-15T06:34:46Z)
• Understanding the Influence of Synthetic Data for Text Embedders [52.04771455432998]
  We first reproduce and publicly release the synthetic data proposed by Wang et al.<n>We critically examine where exactly synthetic data improves model generalization.<n>Our findings highlight the limitations of current synthetic data approaches for building general-purpose embedders.
  arXiv  Detail & Related papers  (2025-09-07T19:28:52Z)
• SynSpill: Improved Industrial Spill Detection With Synthetic Data [3.297182592932918]
  Large-scale Vision-Language Models (VLMs) have transformed general-purpose visual recognition through strong zero-shot capabilities.<n>Their performance degrades significantly in niche, safety-critical domains such as industrial spill detection.<n>We introduce a scalable framework centered on a high-quality synthetic data generation pipeline.
  arXiv  Detail & Related papers  (2025-08-13T20:09:58Z)
• 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)
• OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis [55.390060529534644]
  We propose OS-Genesis, a novel data synthesis pipeline for Graphical User Interface (GUI) agents.<n>Instead of relying on pre-defined tasks, OS-Genesis enables agents first to perceive environments and perform step-wise interactions.<n>We demonstrate that training GUI agents with OS-Genesis significantly improves their performance on highly challenging online benchmarks.
  arXiv  Detail & Related papers  (2024-12-27T16:21:58Z)
• SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection [20.985372561774415]
  We present SyntheWorld, a synthetic dataset unparalleled in quality, diversity, and scale. It includes 40,000 images with submeter-level pixels and fine-grained land cover annotations of eight categories. We will release SyntheWorld to facilitate remote sensing image processing research.
  arXiv  Detail & Related papers  (2023-09-05T02:42:41Z)
• Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances [76.34037366117234]
  We introduce a new dataset called Robot Control Gestures (RoCoG-v2) The dataset is composed of both real and synthetic videos from seven gesture classes. We present results using state-of-the-art action recognition and domain adaptation algorithms.
  arXiv  Detail & Related papers  (2023-03-17T23:23:55Z)
• Domain Adaptation of Synthetic Driving Datasets for Real-World Autonomous Driving [0.11470070927586014]
  Network trained with synthetic data for certain computer vision tasks degrade significantly when tested on real world data. In this paper, we propose and evaluate novel ways for the betterment of such approaches. We propose a novel method to efficiently incorporate semantic supervision into this pair selection, which helps in boosting the performance of the model.
  arXiv  Detail & Related papers  (2023-02-08T15:51:54Z)

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.