Key Points

Blog For QA Only

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Factor	SFT	RLHF
Data	Requires large, high-quality, labeled datasets.	Reduces dependency on labeled data by using dynamic feedback.	Reduces dependency on labeled data by using dynamic feedback.
Task complexity	Well-suited for static, well-defined tasks with clear input-output mappings.	Ideal for complex tasks requiring adaptability, exploration, or complex reasoning.	Ideal for complex tasks requiring adaptability, exploration, or complex reasoning.
Adaptability	Limited; requires retraining for new tasks or changing requirements.	High; adapts dynamically to feedback and changing objectives.	High; adapts dynamically to feedback and changing objectives.
Generalization	Can struggle to generalize to unseen tasks or domains if the training data is not sufficiently diverse.	Promotes better generalization by exploring diverse outputs and optimizing based on feedback.
Implementation complexity	Easier to set up and implement.	More complex to set up, requires RL expertise and infrastructure.
Computational resources	More computationally efficient, especially when labeled data is readily available.	Computationally intensive due to iterative training processes and the need for high-performance hardware.
When to use	Use when you can access substantial labeled data and the task is clearly defined and static.	Use when you need adaptability, have complex objectives, and can incorporate human preferences or feedback.

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