WebXSkill: Skill Learning for Autonomous Web Agents

📰 ArXiv cs.AI

Learn how WebXSkill enables autonomous web agents to learn skills for complex browser tasks, bridging the grounding gap between textual and code-based skills

advanced Published 16 Apr 2026

Action Steps

Implement WebXSkill to bridge the grounding gap between textual and code-based skills
Use large language models (LLMs) to power autonomous web agents
Design workflows with step-level understanding for error recovery
Evaluate the performance of WebXSkill in completing complex browser tasks
Fine-tune WebXSkill to adapt to new workflows and tasks

Who Needs to Know This

AI engineers and researchers working on autonomous web agents can benefit from WebXSkill to improve their agents' ability to complete long-horizon workflows

Key Insight

💡 WebXSkill enables autonomous web agents to learn skills for complex browser tasks by grounding textual workflow skills in executable code

Key Takeaways

Learn how WebXSkill enables autonomous web agents to learn skills for complex browser tasks, bridging the grounding gap between textual and code-based skills

Full Article

Title: WebXSkill: Skill Learning for Autonomous Web Agents

Abstract:
arXiv:2604.13318v1 Announce Type: new Abstract: Autonomous web agents powered by large language models (LLMs) have shown promise in completing complex browser tasks, yet they still struggle with long-horizon workflows. A key bottleneck is the grounding gap in existing skill formulations: textual workflow skills provide natural language guidance but cannot be directly executed, while code-based skills are executable but opaque to the agent, offering no step-level understanding for error recovery

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