Macro-Action Based Multi-Agent Instruction Following through Value Cancellation
This research paper, "Macro-Action Based Multi-Agent Instruction Following through Value Cancellation," explores a new method for AI agents to follow instructions efficiently. It introduces a novel approach using macro-actions and value cancellation to improve multi-agent instruction following. The paper was authored by Wo Wei Lin and two other collaborators. Key details about the paper, including its submission history and full-text access, are available through arXiv. The research is categorized under Artificial Intelligence (cs.AI). Additionally, the broader context of arXivLabs is highlighted, showcasing its role as an experimental platform for developing and integrating new features. arXivLabs operates with a strong commitment to open science, community collaboration, excellence, and user data privacy.
A research paper, "Macro-Action Based Multi-Agent Instruction Following through Value Cancellation," authored by Wo Wei Lin and two collaborators, was submitted to arXiv on May 12, 2026. The paper is available in PDF format and categorized under Artificial Intelligence (cs.AI).
This study introduces a new method for AI agents to follow instructions effectively. It proposes an innovative technique that utilizes macro-actions and value cancellation to enhance instruction following in multi-agent systems.
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