RLHF
Reinforcement learning from human feedback, the alignment technique behind ChatGPT.
/ quick answer
RLHF trains a reward model from human preference pairs, then fine-tunes the base LLM with PPO to maximize that reward. Result: models that follow instructions and refuse unsafe requests. Reinforcement learning from human feedback, the alignment technique behind ChatGPT.
/ frequently asked
What is RLHF?
RLHF trains a reward model from human preference pairs, then fine-tunes the base LLM with PPO to maximize that reward. Result: models that follow instructions and refuse unsafe requests.
What is an example of RLHF?
ChatGPT's instruction-following polish comes from RLHF applied on top of GPT-3.5.
Why does RLHF matter?
Reinforcement learning from human feedback, the alignment technique behind ChatGPT.
What should I learn after RLHF?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
- →DPO (Direct Preference Optimization)
A simpler alternative to RLHF that skips the reward model.
- →Model Distillation
Training a small model to mimic a large one.
- →LoRA
Low-Rank Adaptation: cheap fine-tuning that patches only small matrices.
Related workflows
Turn this into a repeatable process.
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Related tool stacks
The tools that run it in production.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
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Pick between the options.
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