AINeutralarXiv – CS AI · 9h ago6/10
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Discovering Multiagent Learning Algorithms with Large Language Models
Researchers deployed AlphaEvolve, an LLM-powered evolutionary coding framework, to automatically discover new multi-agent reinforcement learning algorithms for imperfect-information games. The system produced two competitive algorithms (VAD-CFR and SHOR-PSRO) that match human-designed baselines, but further analysis revealed that distilled, minimal versions (WOP-CFR and PM-PSRO) generalize better with simpler structures, demonstrating that LLM-discovered complexity often obscures fundamental algorithmic principles.