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@omarsar0

New research from Google DeepMind. What if LLMs could discover entirely new multi-agent learning algorithms? Designing algorithms for multi-agent systems is hard. Classic approaches like PSRO and counterfactual regret minimization took years of expert effort to develop. Each new game-theoretic setting often demands its own specialized solution. But what if you could automate the discovery process itself? This research uses LLMs to automatically generate novel multi-agent learning algorithms through iterative prompting and refinement. The LLM proposes algorithm pseudocode, which gets evaluated against game-theoretic benchmarks, and feedback drives the next iteration. LLMs have absorbed enough algorithmic knowledge from training to serve as creative search engines over the space of possible algorithms. They generate candidates that humans wouldn't think to try. The discovered algorithms achieve competitive performance against established hand-crafted baselines across multiple game-theoretic domains. This shifts algorithm design from manual expert craft to automated discovery. The same approach could generalize beyond games to any domain where we need novel optimization procedures. Paper: https://t.co/9AeQYo2LFS Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

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