arXiv:2606.01993v1 Announce Type: new Abstract: Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks. However, such knowledge is often multimodal, heterogeneous, noisy, and implicitly assumes human executors, making it difficult to use directly as the skills required by agents. To bridge the gap between human-oriented guides and agent-executable skills, we formalize this problem as guide-to-skill learning: converting in-the-wild guides into executable skills and continuously improving them from trajectories observable to the agent. To evalu
The proliferation of multimodal data on the web and advancements in large language models make it increasingly feasible to convert unorganized human knowledge into executable skills for AI agents.
This development is crucial as it outlines a pathway for AI agents to acquire practical skills directly from widespread, 'in-the-wild' information, significantly expanding their capabilities and autonomy.
Agents will be able to more effectively learn and adapt by distilling human-oriented guides into actionable skills, reducing the need for explicit programming or highly structured training data for every task.
- · AI platform developers
- · Automation software companies
- · Industries with abundant procedural documentation
- · Generative AI researchers
- · Manual task training providers
- · Companies reliant on highly bespoke agent programming
- · Systems unable to process multimodal data
AI agents gain the ability to parse complex, multimodal human-generated guides and translate them into operational skills.
This enhanced capability allows agents to perform a wider array of long-horizon tasks across various domains with greater autonomy.
The increased practical utility of self-evolving agents could dramatically reshape service industries and operational workflows, leading to significant productivity gains and job reallocations.
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