SynWeaver: A New Framework for Web Agent Training
A recent study presents SynWeaver, a framework aimed at enhancing the ability of web agents to generalize to unfamiliar websites. This research, accessible on arXiv with the identifier 2608.12429, tackles the shortcomings of existing data synthesis techniques used for training web agents. Such methods frequently do not encompass the complete functionality of websites and may generate fabricated tasks due to inadequate prior knowledge, which hampers the diversity and effectiveness of subsequent trajectory synthesis. SynWeaver conducts a structured exploration of websites to create a comprehensive map that includes functionally varied page states and interactions. It then extracts page-level and transition-level guidance from this map to train a UI-aware model with specific website priors, facilitating more accurate task proposals. The authors highlight this framework as a remedy for the difficulties web agents face in adapting to new websites due to the absence of website-specific supervision. This work is recognized as a cross-type submission.
Key facts
- SynWeaver is a website-prior task-trajectory co-synthesis framework for web agents.
- It addresses limitations in exploration-based data synthesis methods.
- The framework constructs a website map covering functionally distinct page states and executable interactions.
- It derives page-level and transition-level supervision from the map.
- It trains a UI-aware model with website-specific priors.
- The goal is to improve generalization to unseen websites.
- The paper is available on arXiv with identifier 2608.12429.
- The paper is a cross-type announcement.
Entities
Institutions
- arXiv