Verified75% confidenceFactExact time
Supervised Fine-Tuning (SFT) trains the model using 'prompt-response' example pairs, teaching the model to respond in a conversational format, thereby making outputs more coherent.
3
Sources
75%
Confidence
Long-term
Relevance
8/31/2026
First Seen
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Related Claims
VerifiedModern large language models typically go through three training stages: pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF).76% similarVerifiedDomain fine-tuning typically first injects domain knowledge through supervised fine-tuning (SFT), then aligns with user preferences through reinforcement learning from human feedback (RLHF).76% similarUnverifiedIn ChatGPT's training, the pre-training phase uses unsupervised learning for next-token prediction, the instruction fine-tuning phase introduces supervised learning, and finally RLHF is used to align the model's outputs.72% similarUnverified该系统在提示词中明确要求模型只能使用提供的上下文作答，答案不在上下文中时直接回答不知道，而非凭训练记忆编造71% similarUnverifiedLarge model fine-tuning engineers (SFT direction) are one of the fastest-growing areas of enterprise demand, because enterprises often perform SFT supervised fine-tuning and LoRA fine-tuning on existing models using internal data rather than training models from scratch.71% similar
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