Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning
📰 ArXiv cs.AI
Learn to invoke agent skills selectively using dual-granularity preference learning to improve task execution
Action Steps
- Define agentic tasks and identify relevant skills
- Implement dual-granularity preference learning to determine skill invocation
- Train a model to predict skill invocation probabilities
- Test and evaluate the model using simulated tasks
- Refine the model by incorporating feedback and adjusting preference learning parameters
Who Needs to Know This
AI researchers and engineers working on agentic tasks can benefit from this approach to optimize skill invocation and improve overall performance
Key Insight
💡 Selective skill invocation can improve task execution by avoiding unhelpful invocations and reducing irrelevant context
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🤖 Learn to invoke skills selectively in agentic tasks using dual-granularity preference learning! 🚀
Key Takeaways
Learn to invoke agent skills selectively using dual-granularity preference learning to improve task execution
Full Article
Title: Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning
Abstract:
arXiv:2606.00510v1 Announce Type: cross Abstract: Agent skills are callable procedural modules that provide reusable knowledge and execution policies for complex agentic tasks. However, existing methods mainly focus on selecting relevant skills or improving the skills themselves, while overlooking whether a relevant skill should actually be invoked at the current decision point. Unhelpful invocations may introduce irrelevant context and disrupt an otherwise correct execution process. To address
Abstract:
arXiv:2606.00510v1 Announce Type: cross Abstract: Agent skills are callable procedural modules that provide reusable knowledge and execution policies for complex agentic tasks. However, existing methods mainly focus on selecting relevant skills or improving the skills themselves, while overlooking whether a relevant skill should actually be invoked at the current decision point. Unhelpful invocations may introduce irrelevant context and disrupt an otherwise correct execution process. To address
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