• Computer Science > Artificial Intelligence [Submitted on 18 Feb 2026] Title:Multi-agent cooperation through in-context co-player inference View PDF HTML (experimental)Abstract: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.

Article Summaries:

  • Computer Science > Artificial Intelligence [Submitted on 18 Feb 2026] Title:Multi-agent cooperation through in-context co-player inference View PDF HTML (experimental)Abstract: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 s

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