Unverified50% confidenceFactExact time
Pretraining uses self-supervised learning (such as predicting the next word) on massive amounts of unlabeled text to let the model acquire language patterns and world knowledge, and is extremely costly.
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10/31/2026
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UnverifiedLarge model-based agents can leverage world knowledge and language understanding accumulated during pretraining to accomplish tasks with fewer samples compared to pure reinforcement learning solutions79% similarVerifiedSelf-supervised learning is the core paradigm of the pre-training phase, where the model constructs training signals from the data itself through a next-token prediction task.75% similarUnverifiedReasoning models are extremely costly to train, typically requiring a combination of large-scale reinforcement learning and carefully designed reward models to acquire the capability.75% similarUnverified预训练阶段模型通过自监督学习方式（通常是下一个 token 预测任务）在海量文本语料（通常达数万亿 token 规模）上进行训练75% similarUnverifiedCurrent language models mainly rely on offline training and maximum likelihood estimation, lacking theoretical guarantees for online continual learning behavior.74% similar
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