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Communication Enables Cooperation in LLM Agents: A Comparison with Curriculum-Based Approaches
Madmoun, Hachem Lahlou, Salem
Madmoun, Hachem
Lahlou, Salem
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2026.eacl-short.23.pdf
Adobe PDF, 1.91 MB
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Machine Learning
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Conference proceeding
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http://creativecommons.org/licenses/by/4.0/
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English
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Abstract
Eliciting cooperation in multi-agent LLM systems is critical for AI alignment. We investigate two approaches: direct communication and curriculum learning. In a 4-player Stag Hunt, a one-word "cheap talk" channel increases cooperation from 0% to 48.3%, demonstrating communication as a robust coordination mechanism. In contrast, we find that curriculum learning is highly sensitive to design choices: our pedagogical curriculum through progressively complex games reduced agent payoffs by 27.4% in an Iterated Public Goods Game with Punishment. Qualitative analysis reveals that curricula emphasizing defection-equilibrium games can induce "learned pessimism" in agents. These findings suggest that for coordination problems, simple communication protocols may be more reliable than experience-based training, and that curriculum design for social dilemmas requires careful attention to the strategic lessons embedded in game sequences.
Citation
H. Madmoun, S. Lahlou, "Communication Enables Cooperation in LLM Agents: A Comparison with Curriculum-Based Approaches," 2026, pp. 307-321.
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Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics
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The 19th Conference of the European Chapter of the Association for Computational Linguistics
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The 19th Conference of the European Chapter of the Association for Computational Linguistics
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Association for Computational Linguistics