arXiv:2606.04391v1 Announce Type: new Abstract: Language agents increasingly rely on reusable skills to improve multi-step web automation across related tasks. A growing line of work studies online skill learning, where agents continually induce skills from previous task trajectories and reuse them in future tasks on the fly. However, existing methods mainly reuse skills at the task-level: a fixed set of skills is retrieved based on the initial task instruction and then held fixed throughout execution. This static strategy is misaligned with web execution, where the appropriate next action dep
The rapid advancement in large language models and increasing complexity of web tasks necessitate more adaptive and efficient automation techniques, leading to research in online skill learning.
This indicates a significant step towards more autonomous and versatile AI agents capable of continuous improvement and adaptation in dynamic digital environments, impacting productivity and the utility of AI systems.
AI agents will move from static, pre-defined skill sets to dynamically learning and retrieving relevant skills during task execution, enabling performance on a much wider array of complex, multi-step web tasks.
- · AI software developers
- · Businesses adopting automation
- · Cloud providers
- · Tasks requiring repetitive human web interaction
- · Legacy automation software
More robust and flexible AI agents will improve the efficiency of online operations across various industries.
The ability of agents to learn and adapt continuously could lead to faster obsolescence of specialized automation tools and increased demand for general-purpose agent platforms.
Widespread adoption of highly adaptive web agents might accelerate the redefinition of white-collar work, demanding new human skills focused on AI orchestration and oversight rather than execution.
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Read at arXiv cs.AI