Multi-agent cooperation through in-context co-player inference

Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Promising recent work has shown that cooperation can be established between ``learning-aware'' agents that explicitly 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 \textit{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.