Sequence models enable AI agents to cooperate by inferring co-player strategies in-context.
Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Recent work showed that mutual cooperation can be induced between "learning-aware" agents that account for and shape the learning dynamics of their co-players. However, existing approaches typically rely on hardcoded, often inconsistent, assumptions about co-player learning rules or enforce a strict separation between "naive learners" updating on fast timescales and "meta-learners" observing these updates. Here, we demonstrate that the in-context learning capabilities of sequence models allow for co-player learning awareness without requiring hardcoded assumptions or explicit timescale separation. We show that training sequence model agents against a diverse distribution of co-players naturally induces in-context best-response strategies, effectively functioning as learning algorithms on the fast intra-episode timescale. We find that the cooperative mechanism identified in prior work-where vulnerability to extortion drives mutual shaping-emerges naturally in this setting: in-context adaptation renders agents vulnerable to extortion, and the resulting mutual pressure to shape the opponent's in-context learning dynamics resolves into the learning of cooperative behavior. Our results suggest that standard decentralized reinforcement learning on sequence models combined with co-player diversity provides a scalable path to learning cooperative behaviors.
AI agents can learn cooperation through in-context learning without explicit opponent modeling or weight updates.
Heterogeneous agent populations create gradient pressures that drive mutual defection avoidance and genuine cooperation.
Sequence models naturally develop adaptive capabilities that replicate complex multi-agent cooperation dynamics efficiently.
Prior approaches require rigid assumptions about opponent learning or artificial separation of naive and meta-learners.
Standard reinforcement learning struggles with non-stationary environments where all agents simultaneously learn and adapt.
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