Pre-training
The foundational stage in which large language models acquire their capabilities through self-supervised learning—typically next-token prediction—on large-scale text corpora. This stage enables the model to internalize linguistic patterns, world knowledge, and reasoning modes, and determines the model's overall capability ceiling.
Core Facts
The pre-training process of large language models requires thousands of high-end GPUs running in parallel for months, with costs typically ranging from tens of millions to hundreds of millions of dollars.
大语言模型通过在数千亿乃至数万亿个词元（Token）的文本数据上进行自监督预训练
当代前沿模型的训练方法包括RLHF（基于人类反馈的强化学习）及其后继者RLAIF和过程奖励模型（PRM）
Timeline (last 90 days)
Teacher Li Jin suggested an advanced FDE technical path: first do AI agent development, then go deeper into model fine-tuning, distillation and pretraining.
In terms of technical difficulty, the technical threshold of fine-tuning is lower than that of distillation, and distillation is lower than that of pretraining.
Pretraining significantly reduced the number of updates needed to surpass the fixed configuration (roughly halved).
Nathan Lambert believes the science of pretraining itself is not that high a barrier, and that one does not need to spend a billion dollars to get a very good base model; many startups fork and train models based on open-source tools such as Megatron.
Pretraining requires thousands of GPUs and an investment at the multi-million-dollar level, and usually only model vendors or a very few top-tier companies will venture into it.
Pre-training refers to training a base model from scratch on massive amounts of unlabeled text, costing millions of dollars and requiring thousands of GPUs.
During pretraining, the model learns statistical relationships among various tendencies, and these relationships form across the entire corpus rather than existing in any single document.
A pretrained-only model is good at continuing text but not at answering questions or following instructions; Anthropic's glossary states this directly.
During pretraining, the model learns only statistical patterns in text through the next-token prediction objective; no label tells it which statements are true and which are false.
Over-reliance on the fine-tuned part during fine-tuning may be precisely the root cause of rare-class capabilities being obscured; directing the model's computation moderately toward the pretrained components may be a more robust fine-tuning route.
20 more timeline events
All Facts (20)
The pre-training process of large language models requires thousands of high-end GPUs running in parallel for months, with costs typically ranging from tens of millions to hundreds of millions of dollars.
90%Verified大语言模型通过在数千亿乃至数万亿个词元（Token）的文本数据上进行自监督预训练
80%Verified当代前沿模型的训练方法包括RLHF（基于人类反馈的强化学习）及其后继者RLAIF和过程奖励模型（PRM）
80%Verified大语言模型的预训练阶段本质上属于自监督学习，是一种特殊的非监督学习，模型通过预测下一个词来从海量文本中自动学习语言规律
80%VerifiedReinforcement learning training is the key stage in which a model transitions from general language capability to precise task execution, further optimizing reasoning, planning, and instruction-following through reinforcement learning after large-scale pre-training.
75%VerifiedA 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.
75%VerifiedIn-context learning was first observed in large language models, and its capability fundamentally stems from the diversity of pretraining data.
75%VerifiedLarge model training goes through three core stages: pre-training, supervised fine-tuning (SFT), and preference alignment.
75%Verified仅用成功轨迹训练的模型往往存在分布外泛化的严重缺陷，遇到工具返回错误时便丧失纠偏能力
70%VerifiedTechniques such as mixed-precision training and gradient accumulation enable training of larger models under limited GPU memory.
65%Unverified训练数据在输入模型前会经过严格的去重、过滤和质量筛选，低质量内容、重复文本、有害内容会被剔除
90%UnverifiedA language model in the pre-training stage only learns to continue text and does not understand the concept of answering questions or following instructions
60%UnverifiedAI的训练过程分为三个阶段：预训练、后训练和对齐
55%UnverifiedIn terms of technical difficulty, the technical threshold of fine-tuning is lower than that of distillation, and distillation is lower than that of pretraining.
50%UnverifiedTeacher Li Jin suggested an advanced FDE technical path: first do AI agent development, then go deeper into model fine-tuning, distillation and pretraining.
50%UnverifiedPretraining significantly reduced the number of updates needed to surpass the fixed configuration (roughly halved).
50%UnverifiedNathan Lambert believes the science of pretraining itself is not that high a barrier, and that one does not need to spend a billion dollars to get a very good base model; many startups fork and train models based on open-source tools such as Megatron.
50%UnverifiedPretraining requires thousands of GPUs and an investment at the multi-million-dollar level, and usually only model vendors or a very few top-tier companies will venture into it.
50%UnverifiedPre-training refers to training a base model from scratch on massive amounts of unlabeled text, costing millions of dollars and requiring thousands of GPUs.
50%UnverifiedDuring pretraining, the model learns statistical relationships among various tendencies, and these relationships form across the entire corpus rather than existing in any single document.
50%