Verified65% confidenceFactExact time
Domain fine-tuning typically first injects domain knowledge through supervised fine-tuning (SFT), then aligns with user preferences through reinforcement learning from human feedback (RLHF).
3
Sources
65%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
A Beginner's Guide to Claude Code: Why Is It More Powerful Than Cursor and Trae?
bilibili资深bug设计工程师7/7/2026
Related Claims
VerifiedInstruction fine-tuning was systematically proposed by InstructGPT (2022), combining reinforcement learning from human feedback (RLHF) to make models better aligned with human intent.77% similarVerifiedRLHF（基于人类反馈的强化学习）是ChatGPT等对话模型背后的关键训练技术76% similarUnverifiedSupervised 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.76% similarVerifiedModern large language models typically go through three training stages: pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF).75% similarVerifiedSome systems embed safety preferences into model weights during the training phase through RLHF (Reinforcement Learning from Human Feedback), causing the model itself to tend to refuse generating certain content rather than relying on external filtering.75% similar
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