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This position paper argues that Large Language Models (LLMs), despite their current utility as Artificial Useful Intelligence (AUI), often struggle with robust and adaptive reasoning required for Artificial General Intelligence (AGI) because their training methods overfit to specific data patterns. The authors propose a shift from the current supervised pretraining (SPT) paradigm to reward-based pretraining (RPT), similar to how AlphaZero su

General Intelligence Requires Reward-based Pretraining

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