Model Training

RLHF

Reinforcement Learning from Human Feedback

Quick Answer: A training technique where human evaluators rank model outputs by quality, and these rankings are used to train a reward model that guides the language model toward more helpful, harmless, and honest responses.

Reinforcement Learning from Human Feedback is a training technique where human evaluators rank model outputs by quality, and these rankings are used to train a reward model that guides the language model toward more helpful, harmless, and honest responses.

Example

Human evaluators compare two model responses to the same prompt and select the better one. After thousands of these comparisons, the model learns to generate responses that align with human preferences for helpfulness and safety.

Why It Matters

RLHF is why modern chatbots feel helpful rather than just generating text. It's the technique that made ChatGPT usable. Understanding RLHF helps prompt engineers understand why models behave the way they do.

How It Works

RLHF (Reinforcement Learning from Human Feedback) is the training technique that transforms raw language models into helpful, safe assistants. The process has three stages: supervised fine-tuning on high-quality demonstrations, training a reward model on human preference comparisons, and optimizing the language model against the reward model using reinforcement learning (typically PPO).

The preference data collection is critical: human raters compare pairs of model outputs and indicate which one is better. These comparisons train the reward model to score outputs by quality. The language model then learns to generate outputs that score highly.

RLHF has largely been superseded by simpler alternatives like DPO (Direct Preference Optimization) and ORPO, which achieve similar results without the complexity of training a separate reward model. However, understanding RLHF remains essential because it explains why modern AI assistants behave the way they do.

Common Mistakes

Common mistake: Thinking RLHF is just about safety and content filtering

RLHF shapes all aspects of model behavior: helpfulness, formatting, tone, verbosity, and reasoning quality. It's why models answer questions instead of just completing text.

Common mistake: Assuming RLHF-trained models are unbiased because humans provided the feedback

Human raters have their own biases, which get baked into the reward model. RLHF-trained models can exhibit systematic biases from the preference data.

Career Relevance

Understanding RLHF is important for AI researchers and ML engineers working on model training. For prompt engineers, it provides crucial context for why models behave certain ways and how to work with (rather than against) their training.

Related Terms

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