Comparison
Reinforcement Learning from Human Feedback vs Synthetic Data
Reinforcement Learning from Human Feedback and Synthetic Data are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Reinforcement Learning from Human Feedback
Reinforcement Learning from Human Feedback comes up when the question is fundamentally about training.
ChatGPT trained with RLHF to refuse unsafe requests.
When you would reach for Synthetic Data
Synthetic Data comes up when the question is fundamentally about training.
Phi-3 trained heavily on textbook-quality synthetic data.
Frequently asked
What is the difference between Reinforcement Learning from Human Feedback and Synthetic Data?
Reinforcement Learning from Human Feedback: RLHF fine-tunes an LLM to maximize a reward model that was itself trained on human preference judgments between candidate responses. Synthetic Data: Synthetic data is training data produced by a model — instructions distilled from GPT-4, code generated and filtered by tests, reasoning traces sampled from a stronger model — rather than handwritten by humans.
When should I use Reinforcement Learning from Human Feedback vs Synthetic Data?
Reinforcement Learning from Human Feedback is the right concept when you are focused on training. Synthetic Data applies when you are focused on training.
Are Reinforcement Learning from Human Feedback and Synthetic Data the same thing?
No. Reinforcement Learning from Human Feedback is training; Synthetic Data is training. They are related but address different parts of the AI stack.