Partially Verified65% confidenceFactExact time
Pre-training uses self-supervised learning through next-token prediction on large-scale unlabeled corpora
3
Sources
65%
Confidence
Long-term
Relevance
8/2/2026
First Seen
Sources
Training a 1.3B Parameter LLM from Scratch: Complete Workflow and Core Challenges
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Related Claims
VerifiedSelf-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.74% similarUnverifiedDecision-focused learning (Predict-then-Optimize) advocates using downstream decision quality directly as the training signal.70% 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70% similarVerifiedA pre-trained model refers to a model that has been trained in advance on large-scale datasets, which users can directly fine-tune or call to complete specific tasks.69% similarUnverifiedThe in-context predictor is optimal for topics never seen during training; it relies on the prompt for on-the-fly adaptation rather than on stored parameters.68% similar
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