From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?
Binyan Xu, Dong Fang, Haitao Li, Kehuan Zhang
cs.AI
Submitted: 2026-08-19
Updated: 2026-08-20
Code: https://github.com/Tencent/AdaSkill
Project page: https://adaptive-skill.github.io/Codehttps://github.com/Tencent/AdaSkill
Terminology
Sources
- GPT-4 Technical Report
- SMAGDi: Socratic Multi Agent Interaction Graph Distillation for Efficient High Accuracy Reasoning
- EvoSkill: Automated Skill Discovery for Multi-Agent Systems
- APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL
- DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
- Distilling the Knowledge in a Neural Network
- When Single-Agent with Skills Replace Multi-Agent Systems and When They Fail
- AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent
- Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
- FELA: A Multi-Agent Evolutionary System for Feature Engineering of Industrial Event Log Data
- Scaling Large Language Model-based Multi-Agent Collaboration
- Voyager: An Open-Ended Embodied Agent with Large Language Models
- Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents
- SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
- When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting
- Contextual Agentic Memory is a Memo, Not True Memory
- LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception
- From Internal Diagnosis to External Auditing: A VLM-Driven Paradigm for Data-Free Online Backdoor Defense
- Rethinking the Value of Multi-Agent Workflow: A Strong Single Agent Baseline
- AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
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