Supervised Fine-Tuning (SFT)
Definition
Why "Supervised Fine-Tuning (SFT)" Matters in AI
Understanding supervised fine-tuning (sft) is essential for anyone working with artificial intelligence tools and technologies. This training-related concept is crucial for understanding how AI models learn and improve over time. Whether you're a developer, business leader, or AI enthusiast, grasping this concept will help you make better decisions when selecting and using AI tools.
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Frequently Asked Questions
What is Supervised Fine-Tuning (SFT)?
Initial training phase in RLHF where model learns from labeled examples before preference learning....
Why is Supervised Fine-Tuning (SFT) important in AI?
Supervised Fine-Tuning (SFT) is a intermediate concept in the training domain. Understanding it helps practitioners and users work more effectively with AI systems, make informed tool choices, and stay current with industry developments.
How can I learn more about Supervised Fine-Tuning (SFT)?
Start with our AI Fundamentals course, explore related terms in our glossary, and stay updated with the latest developments in our AI News section.