RLHF (Reinforcement Learning from Human Feedback)
What is RLHF?
Reinforcement Learning from Human Feedback (RLHF) is a training technique used to align Language Models with human values and preferences. It's the "secret sauce" that turned raw GPT-3 into the helpful ChatGPT assistant we use today.
The Core Idea: Train a reward model based on human rankings of AI outputs, then use that reward model to fine-tune the LLM to generate "better" answers.
Three Steps of RLHF:
- Supervised Fine-Tuning (SFT): Train model on human demonstrations.
- Reward Modeling: Humans rank multiple model outputs; train a "Reward Model" to predict these rankings.
- Reinforcement Learning (PPO): Optimize the LLM to maximize the score from the Reward Model.
Frontend Relevance
You Will Not Implement This
RLHF is extremely expensive and complex. It requires massive datasets of human feedback and significant GPU resources. As a frontend engineer, you only need to know that this is why models refuse to answer harmful questions or try to be "helpful, honest, and harmless."
Used By
Virtually all top-tier Chat models use RLHF or similar alignment techniques (like RLAIF - AI Feedback):
- OpenAI: ChatGPT (GPT-3.5, GPT-4)
- Anthropic: Claude (Constitutional AI, a variation of RLHF)
- Meta: Llama 2 & 3 (Llama-2-chat)
- Google: Gemini
Why It Matters for Application Development
Understanding RLHF helps explain certain model behaviors:
- Refusals: Models might refuse benign requests because of over-optimization on safety data during RLHF.
- Verbosity: Models often become "chatty" or hedge their answers ("As an AI language model...") due to RLHF patterns.
- Style: The "personality" of the model is largely shaped during this stage.