让AI群体自己决定谁干活，比中央指挥更靠谱
现在的多智能体系统，分工是提前写死的：谁负责找路、谁负责搬东西，都是设计者定好的。这篇论文反着来——让智能体自己“投标”决定谁上场。每个智能体根据当前情况报一个价，出价高的就干活，干完活根据集体成果分“工资”，这个工资又反过来训练它下次怎么出价、怎么行动。结果是在造东西、机器人导航、深度研究三类任务上，这种“市场式”自我编排比固定分工强，而且群体越大表现越好，中途掉几个成员损失也更小。它不是你明天能用上的东西，但指向一个更本质的问题：当AI数量多到人类没法逐个指挥时，让它们自己用“市场”协调，可能比设计更复杂的中央调度更靠谱。
📄 原文摘要(英文)
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.