Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
SCAFFOLD: Self-Improving Web Agents via Recursive Parametric Skill Abstraction
TL;DR
SCAFFOLD is a self-improving framework for visual web agents that enhances their ability to learn and adapt by recursively composing skills. It achieves an 11.1-17.2% improvement in success rates over existing methods across multiple testing environments.
✦ Why It Matters
Implement SCAFFOLD to enhance your web agents' learning capabilities and improve task success rates immediately.
Key Takeaways
- Explore SCAFFOLD for developing adaptive web agents.
- Utilize parametric skills to enhance task performance.
- Implement hierarchical skill structures for better organization.
- Apply MDL criteria to optimize skill libraries.
How It Works
SCAFFOLD induces skills from successful task trajectories, creating a hierarchy where higher-level skills can invoke lower-level ones. This recursive composition allows for more complex task handling.
The framework uses a minimum-description-length (MDL) approach to compact the skill library, ensuring that redundant skills are minimized while maintaining behavioral equivalence. By periodically distilling learned skills back into the model's weights, SCAFFOLD internalizes these abstractions, leading to continuous improvement.