Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
📰 ArXiv cs.AI
Trace2Skill framework distills trajectory-local lessons into transferable agent skills for Large Language Model (LLM) agents
Action Steps
- Identify trajectory-local lessons from agent trajectories
- Distill these lessons into transferable skills using the Trace2Skill framework
- Integrate the distilled skills into LLM agents to improve their performance on complex tasks
- Evaluate the effectiveness of the Trace2Skill framework in various domains and tasks
Who Needs to Know This
AI researchers and engineers on a team can benefit from Trace2Skill to improve the scalability and generalizability of automated skill generation for LLM agents, enabling them to tackle complex tasks more effectively
Key Insight
💡 Trace2Skill overcomes the scalability bottleneck of manual authoring and the fragility of automated skill generation by leveraging trajectory-local lessons to create generalizable skills
Share This
🤖 Introducing Trace2Skill: a framework to distill trajectory-local lessons into transferable agent skills for LLM agents! 💡
Key Takeaways
Trace2Skill framework distills trajectory-local lessons into transferable agent skills for Large Language Model (LLM) agents
Full Article
Title: Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
Abstract:
arXiv:2603.25158v1 Announce Type: new Abstract: Equipping Large Language Model (LLM) agents with domain-specific skills is critical for tackling complex tasks. Yet, manual authoring creates a severe scalability bottleneck. Conversely, automated skill generation often yields fragile or fragmented results because it either relies on shallow parametric knowledge or sequentially overfits to non-generalizable trajectory-local lessons. To overcome this, we introduce Trace2Skill, a framework that mirro
Abstract:
arXiv:2603.25158v1 Announce Type: new Abstract: Equipping Large Language Model (LLM) agents with domain-specific skills is critical for tackling complex tasks. Yet, manual authoring creates a severe scalability bottleneck. Conversely, automated skill generation often yields fragile or fragmented results because it either relies on shallow parametric knowledge or sequentially overfits to non-generalizable trajectory-local lessons. To overcome this, we introduce Trace2Skill, a framework that mirro
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