CooT: Learning to Coordinate In-Context with Coordination Transformers

Effective coordination among artificial agents in dynamic and uncertain environments remains a significant challenge in multi-agent systems. Existing approaches, such as self-play and population-based methods, either generalize poorly to unseen partners or require impractically extensive training. To overcome these limitations, we propose Coordination Transformers (CooT), a novel in-context coordination framework that uses recent interaction histories to rapidly adapt to unseen partners. Unlike previous approaches that primarily aim to increase the diversity of training partners, CooT explicitly focuses on adapting to new partner behaviors by predicting actions aligned with observed partner interactions. Trained on interaction trajectories collected from diverse pairs of agents with complementary behaviors, CooT quickly learns effective coordination strategies without explicit supervision or fine-tuning. Evaluations on the Overcooked benchmark demonstrate that CooT significantly outperforms baseline methods in coordination tasks involving previously unseen partners. Human evaluations further confirm CooT as the most effective collaborative partner, while extensive ablations highlight its robustness, flexibility, and sensitivity to context in multi-agent scenarios.