MetaAgent-X: End-to-End RL for Multi-Agent Systems
MetaAgent-X, a novel AI framework, brings forth end-to-end reinforcement learning tailored for automatic multi-agent systems. In contrast to earlier methods that either rely on training-free test-time searches or solely enhance the meta-level designer while leaving execution agents inactive, MetaAgent-X simultaneously refines both design and execution processes. This framework facilitates the generation of script-based MAS, the collection of execution rollout data, and the distribution of credit for both designer and executor trajectories. To achieve stable and scalable optimization, it introduces Executor Designer Hierarchical Rollout and Stagewise Co-evolution. This research was published on arXiv (2605.14212v1) and tackles the limitations posed by the frozen-executor ceiling in current automatic MAS methodologies.
Key facts
- MetaAgent-X is an end-to-end reinforcement learning framework for automatic multi-agent systems.
- It jointly optimizes automatic MAS design and execution.
- The framework enables script-based MAS generation, execution rollout collection, and credit assignment.
- It proposes Executor Designer Hierarchical Rollout and Stagewise Co-evolution for training stability.
- The paper is published on arXiv with ID 2605.14212v1.
- It addresses the frozen-executor ceiling in existing automatic MAS approaches.
Entities
Institutions
- arXiv