![[2602.16301] Multi-agent cooperation through in-context co-player inference](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16301] Multi-agent cooperation through in-context co-player inference
Summary
This paper explores multi-agent cooperation in reinforcement learning through in-context learning, demonstrating how sequence models can facilitate cooperative behavior without hardcoded assumptions.
Why It Matters
Understanding how agents can cooperate in multi-agent environments is crucial for advancing artificial intelligence. This research offers a novel approach that leverages in-context learning, potentially leading to more effective and scalable cooperative strategies in AI systems.
Key Takeaways
- In-context learning allows agents to adapt their strategies based on co-player behavior.
- The study shows that cooperation can emerge naturally from diverse co-player interactions.
- Standard decentralized reinforcement learning can be enhanced by integrating co-player diversity.
- Agents become vulnerable to extortion, which drives mutual shaping and cooperation.
- This research provides a scalable path for developing cooperative behaviors in AI.
Computer Science > Artificial Intelligence arXiv:2602.16301 (cs) [Submitted on 18 Feb 2026] Title:Multi-agent cooperation through in-context co-player inference Authors:Marissa A. Weis, Maciej Wołczyk, Rajai Nasser, Rif A. Saurous, Blaise Agüera y Arcas, João Sacramento, Alexander Meulemans View a PDF of the paper titled Multi-agent cooperation through in-context co-player inference, by Marissa A. Weis and 6 other authors 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 vulnerab...
