外观
论文arXiv 2610.2232213 天前
在去中心化智能体群体中学习自编排
原文链接：Learning Self-Orchestration in Decentralized Agent Swarms（arXiv 2610.22322 · 发布日期：2026-10-15）
Learning Self-Orchestration in Decentralized Agent Swarms
提出MPT，通过竞价联合学习动作策略与自编排，并在具身制作、机器人导航和深度研究上验证收益。
核心要点
- 现有多智能体常在固定编排结构中训练，割裂个体能力与集体组织学习。
- MPT让智能体以出价表达参与意愿，决定行动者并为集体结果分配本地训练信用。
- 在三类任务上优于单体后训练和其他协调策略，参与模式随学习演化。
- 消融显示动作与出价均受益；代码和训练模型已在GitHub发布。
验证范围与局限：验证范围为摘要列出的具身制作、机器人导航和深度研究任务。
英文摘要 (Abstract)
The capabilities of an agent swarm depend on both its individual members and how they coordinate. Yet existing multi-agent systems often train agents within fixed orchestration structures, separating the learning of individual capabilities from the organization of collective behavior. We introduce market-based post-training (MPT), a framework for jointly learning action policies and self-orchestration in decentralized agent swarms. Agents express their willingness to participate through bids, which determine who acts and how collective outcomes provide local training credit. Learning both action and bidding behavior allows individual capabilities and patterns of participation to adapt together without a central coordinating agent. We evaluate MPT on embodied crafting, robot navigation, and deep research. Results show gains over monolithic post-training and confirm that agent participation indeed evolves with learning. With the same post-trained action policies, learned self-orchestration outperforms alternative coordination strategies. Ablations show that both action and bidding benefit from learning, and that learned bids provide more effective action-training signals. Beyond the original training objectives, trained swarms adapt faster to related tasks and achieve better performance with larger populations at inference time. Larger populations also suffer smaller performance losses under agent dropout, demonstrating that learned self-orchestration supports reuse, scaling, and robustness in decentralized agent swarms. Code and trained models are available at https://github.com/Embodied-Minds-Lab/MPT.
作者：Guowei Xu, Zhenting Qi, Amani Kiruga, Himabindu Lakkaraju, Yilun Du
来源
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