RLHF (Reinforcement Learning from Human Feedback)
A technique to fine-tune AI models using human preferences, making outputs more helpful, harmless, and aligned with human values.
In-Depth Explanation
Reinforcement Learning from Human Feedback (RLHF) is a training technique that aligns AI models with human preferences. Rather than optimising for next-token prediction alone, RLHF optimises for human-judged quality and helpfulness.
How RLHF works:
- Supervised fine-tuning: Train on human-written responses
- Reward model training: Learn to predict human preferences
- RL optimisation: Fine-tune policy to maximise reward
The RLHF process:
- Collect comparison data: humans rank model outputs
- Train reward model to predict rankings
- Use PPO or similar to optimise the language model
- Iterate with more human feedback
Why RLHF matters:
- Produces more helpful responses
- Reduces harmful outputs
- Better follows instructions
- More natural conversation
- Aligns with human values
This technique is why ChatGPT feels helpful and conversational rather than just completing text.
Business Context
RLHF is why ChatGPT feels helpful and safe. It's complex to implement but crucial for public-facing AI applications.
How Clever Ops Uses This
While most businesses use pre-RLHF'd models, understanding RLHF helps Australian businesses evaluate model choices and understand why different models behave differently.
Example Use Case
"Training a model to prefer helpful responses over technically correct but unhelpful ones - this is what makes ChatGPT conversational."
Frequently Asked Questions
Related Terms
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Fine-Tuning
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Training
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