ALEM benchmark reveals LLM agents achieve only ~6% normalized return in open-ended multi-agent settings, with communication as the main driver of coordination and individual task competence not implying coordination competence.
An efficient open world environment for multi-agent social learning
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The paper argues for modeling ethics in RL as relatively stable habits and dispositions rather than rules or scalar rewards, and provides a four-part roadmap using social learning, multi-objective methods, regularization, and diverse ethical traditions.
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Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
ALEM benchmark reveals LLM agents achieve only ~6% normalized return in open-ended multi-agent settings, with communication as the main driver of coordination and individual task competence not implying coordination competence.
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Toward Virtuous Reinforcement Learning: A Critique and Roadmap
The paper argues for modeling ethics in RL as relatively stable habits and dispositions rather than rules or scalar rewards, and provides a four-part roadmap using social learning, multi-objective methods, regularization, and diverse ethical traditions.