RELIC: Privacy-Preserving Skill Learning for Multi-Agent Planning
RELIC is a framework that enables agents to learn and transfer composable skills in multi-agent planning scenarios without sharing internal code. Instead, agents abstract successful behaviors into general principles, which can be instantiated by others with different interfaces. Skill refinement occurs via private LLM-guided search, and a trusted orchestrator evaluates updates based on overall team performance. This approach allows for coordination and skill transfer while preserving privacy and accommodating heterogeneous agent designs.
Why it matters: RELIC introduces a new paradigm for privacy-preserving coordination in multi-agent systems, addressing a key challenge in scenarios where agents cannot share internal implementations.
Full story at: arXiv AI/ML ↗