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LLMs can learn to play complex games far more effectively by co-evolving a skill bank with a decision-making agent, enabling consistent long-horizon decision-making.
Despite architectural differences, language models exhibit convergent evolution by learning similar periodic features for number representation, but achieving geometric separability depends on subtle training factors.
