Unverified50% confidenceBenchmarkExact time
MPT's comparative experiments show that under the same data and compute budget, letting agents learn strategies separately and coordinate through bidding not only achieves better task performance but also remains stable when the number of agents changes.
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10/16/2026
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UnverifiedThe research team evaluated MPT on three tasks—embodied crafting, robot navigation, and deep research—and the results show that its performance surpasses monolithic post-training, and that agents' participation patterns evolve with learning.77% similarUnverifiedExperiments found that when the evaluation environment shifts, LLM agents demonstrate significantly better adaptability than reinforcement learning agents.72% similarUnverifiedMPC is better suited for scenarios requiring rapid adaptation, while training policies within simulation often has greater advantages in sample efficiency and final performance.71% similarUnverifiedThe core mechanism of MPT is to let agents express their willingness to participate through bidding; bids determine who takes action and how the collective outcome provides local training credit, thereby enabling joint learning of action strategies and bidding behavior without a central coordinator.71% similarUnverified实验表明按由易到难顺序喂给模型训练样本能加速收敛并学到更鲁棒的特征表示，性能优于随机打乱顺序的训练70% similar
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