CooT: Learning to Coordinate In-Context with Coordination Transformers
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911693299253248 |
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| author | Wang, Huai-Chih Chuang, Hsiang-Chun Cheng, Hsi-Chun Wu, Dai-Jie Sun, Shao-Hua |
| author_facet | Wang, Huai-Chih Chuang, Hsiang-Chun Cheng, Hsi-Chun Wu, Dai-Jie Sun, Shao-Hua |
| contents | Effective coordination among unfamiliar partners remains a major challenge in multi-agent systems. Existing approaches, such as population-based methods, improve robustness through diversity but often lack mechanisms for efficient adaptation beyond training distribution. Moreover, fine-tuning is impractical in few-shot settings due to its high interaction cost. To address these limitations, we propose CooT, a framework that leverages in-context learning (ICL) for real-time partner adaptation. Unlike prior ICL approaches that focus on task generalization, CooT is designed to generalize across diverse partner behaviors. Trained on trajectories from behavior-preferring agents, it learns to align actions with partner intentions purely through observation. We evaluate CooT on two challenging multi-agent benchmarks: Overcooked and Google Research Football. Results show that CooT consistently outperforms population-based methods, gradient-based fine-tuning, and Meta-RL baselines, achieving stable and rapid adaptation without parameter updates. Human evaluations also identify CooT as a preferred collaborator, and our ablations confirm its ability to adapt quickly to new partners and remain stable under sudden partner changes, making it reliable for real-world human-AI collaboration. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_23549 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CooT: Learning to Coordinate In-Context with Coordination Transformers Wang, Huai-Chih Chuang, Hsiang-Chun Cheng, Hsi-Chun Wu, Dai-Jie Sun, Shao-Hua Artificial Intelligence Human-Computer Interaction Machine Learning Effective coordination among unfamiliar partners remains a major challenge in multi-agent systems. Existing approaches, such as population-based methods, improve robustness through diversity but often lack mechanisms for efficient adaptation beyond training distribution. Moreover, fine-tuning is impractical in few-shot settings due to its high interaction cost. To address these limitations, we propose CooT, a framework that leverages in-context learning (ICL) for real-time partner adaptation. Unlike prior ICL approaches that focus on task generalization, CooT is designed to generalize across diverse partner behaviors. Trained on trajectories from behavior-preferring agents, it learns to align actions with partner intentions purely through observation. We evaluate CooT on two challenging multi-agent benchmarks: Overcooked and Google Research Football. Results show that CooT consistently outperforms population-based methods, gradient-based fine-tuning, and Meta-RL baselines, achieving stable and rapid adaptation without parameter updates. Human evaluations also identify CooT as a preferred collaborator, and our ablations confirm its ability to adapt quickly to new partners and remain stable under sudden partner changes, making it reliable for real-world human-AI collaboration. |
| title | CooT: Learning to Coordinate In-Context with Coordination Transformers |
| topic | Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2506.23549 |