概述
The choice matters because the first mainly shifts what a model knows about a domain and its language, the second mainly shifts how it responds, and the data and compute needed differ greatly.
深入探讨
Continued pretraining, also called domain-adaptive pretraining, picks up where the original pretraining stopped. The model sees raw text from the target domain, such as medical papers, source code or legal filings, and learns to predict every next token, exactly as in pretraining. Gururangan and colleagues showed in the 2020 paper Don't Stop Pretraining that a second phase of pretraining on in-domain text improved RoBERTa on tasks in biomedical, computer science, news and review domains. It usually needs large volumes of text, often billions of tokens, and full-parameter training, so it costs far more than fine-tuning. Fine-tuning, usually supervised fine-tuning, uses prompt and response pairs. Loss is typically computed only on the response tokens, so the model learns what to produce given an input. Datasets range from a few thousand to hundreds of thousands of examples, and parameter-efficient methods such as LoRA make it affordable on modest hardware. It is good at teaching format, style, task behavior and instruction following. Preference methods such as RLHF or DPO often follow. A common pipeline is base model, then continued pretraining, then supervised fine-tuning, then preference tuning. Continued pretraining has a known risk: catastrophic forgetting, where general skills degrade. Applied to a chat model, it can also erode instruction following, so teams often start from a base model and redo instruction tuning afterward. Two misconceptions stand out. First, fine-tuning is not a reliable way to add many new facts; research such as Gekhman and colleagues (2024) found models learn new facts slowly through fine-tuning and that doing so can increase hallucination. For fast-changing facts, retrieval is often the better tool. Second, BloombergGPT is sometimes cited as continued pretraining, but it was trained from scratch on a mix of financial and general data. A rough rule: use continued pretraining when domain language differs a lot from web text and unlabeled data is plentiful; use fine-tuning when you need a behavior or format.
战略影响
成本与预算
多年来，架构决策决定着性能和运营成本。
更清晰的判决
技术教育帮助团队选择正确的堆栈，而不仅仅是最新的堆栈。
质量控制
更好的工程选择可以减少生产中的可靠性事故。
The Future of Continued Pretraining vs Fine-Tuning
The steady release of capable open base models has made continued pretraining practical for more organizations, though it still requires substantial data and compute. Active research areas include reducing forgetting, making domain adaptation cheaper, and combining weights from separately trained models. In practice, many teams will keep pairing light fine-tuning with retrieval rather than full continued pretraining, reserving the heavier option for domains whose language is poorly covered by general web data.
现实世界的实施
Code Llama was built by continuing to train Llama 2 on a large corpus of code, and some of its variants were then further trained for Python and for following instructions.
A legal-tech company with millions of unlabeled contracts and filings runs continued pretraining to improve the model's handling of legal language, then fine-tunes on a few thousand annotated clause-extraction examples.
A bank wants a chatbot that answers in a fixed JSON format with a set tone; supervised fine-tuning, or even careful prompting, is enough because the model already knows the general content.
EPFL's Meditron models adapted Llama 2 to medicine through continued pretraining on medical papers and clinical guidelines before any task-specific fine-tuning.
风险与防护栏
优化一项基准测试可以隐藏更广泛的系统弱点。
基础设施和维护成本常常被低估。
随着系统变得更加复杂，安全性和可观察性差距可能会扩大。
实施路线图
在实施之前定义延迟、质量和成本目标。
在实际负载和数据条件下进行基准测试。
仪器监控错误、漂移和用户影响。
在扩展之前准备回滚和事件响应路径。
不断探索
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常见问题
What is Continued Pretraining vs Fine-Tuning?
Continued pretraining keeps training a base model with the same next-token objective on large amounts of raw domain text, while fine-tuning trains on a smaller curated set of labeled examples, such as instruction and response pairs, to shape behavior. The choice matters because the first mainly shifts what a model knows about a domain and its language, the second mainly shifts how it responds, and the data and compute needed differ greatly.
What is the main data difference between continued pretraining and supervised fine-tuning?
Continued pretraining predicts next tokens on raw text, while fine-tuning learns from prompt and response pairs.
In supervised fine-tuning, where is the loss usually computed?
Masking the prompt means the model learns what to produce given an input, not to reproduce the input.
What is replay in continued pretraining?
Including general data helps keep general skills from degrading while the model learns the domain.
How was Code Llama built?
Code Llama is a continued pretraining example, with some variants further trained for Python and instructions.
For facts that change often, which approach does the guide suggest is often better than fine-tuning?
Fine-tuning learns new facts slowly and can increase hallucination, so retrieval is often better for changing information.
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