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Multi-agent LLMs can now continually optimize their skills, leading to unprecedented adaptability and performance in complex tasks.
LLM agents can learn to cooperate far more efficiently by borrowing credit assignment techniques from classic multi-agent RL.
LLMs learn faster and perform better in decision-making tasks when rewarded for being uncertain, not just for succeeding.
