Reinforcement learning with human feedback

As explained in Introduction to Generative AI, Second Edition

Reinforcement learning from human feedback (RLHF) is a technique that uses human evaluations or preferences to estimate rewards and optimize model behavior.

Reinforcement learning from human feedback (RLHF) steers a language model by having people judge responses as acceptable, problematic, or preferable. Those judgments estimate a reward function, which then guides the model toward responses that human evaluators rate more favorably. Because evaluators can disagree, the resulting reward approximates rather than perfectly defines ideal behavior. section 2.10

RLHF is useful but not cost-free:

• It can produce more robust behavioral changes than supervised fine-tuning, while requiring additional data to train a reward model. section 2.11 section 3.8
• Direct preference optimization (DPO) trains directly on human preference pairs, avoiding the intermediate reward model and often reducing complexity and computation. section 2.11
• RLHF can accommodate varied feedback and complex reward functions, whereas the original DPO formulation relies on binary comparisons between two responses. section 2.11

Safety post-training

Providers use RLHF among other post-training methods to steer responses away from harms such as privacy violations and dangerous misinformation. This work can expose outsourced annotators to traumatic material, while disagreement about what counts as safe makes the target behavior contested rather than purely technical. section 3.8

Figure 2.7 A typical setup for reinforcement learning from human feedback, after a supervised fine-tuning step for dialogue

Shows a typical RLHF arrangement for dialogue after supervised fine-tuning, clarifying how human feedback enters the training process. section 2.10

How Introduction to Generative AI, Second Edition covers it

How deep it goes

Explains evaluation, reward estimation, and model improvement conceptually, while deferring implementation details and exact optimization procedures.

Examples

Uses dialogue responses, preference labels, unsafe-input responses, and content such as hate speech, sexual abuse, and violence.

What it assumes

Assumes basic awareness of language-model pretraining and supervised fine-tuning, while introducing reward estimation and preference-based steering.

What you can do after

Evaluate/understand how human preferences shape model behavior and why reward-model design, data demands, and labor conditions matter.

How it connects

Connects reinforcement learning with reward functions, supervised fine-tuning, direct preference optimization, and AI feedback.

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Also explained in

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Related concepts

• reward modeluses · A reward model is a learned model that assigns scores to outputs, actions, or outcomes to represent their desirability according to preferences or evaluation criteria.
• Proximal Policy Optimizationuses · Proximal Policy Optimization is a policy-gradient reinforcement-learning algorithm that limits policy updates to keep successive policies close and promote stable learning.
• reinforcement learninguses · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
• Direct Preference Optimizationcontrasts with · Direct Preference Optimization is a technique for training a model directly from preference data, such as preferred and dispreferred outputs, without separately fitting a reward model.
• RLAIFalternative to · Reinforcement learning from AI feedback is a training or alignment technique that uses AI-generated evaluations or preferences as feedback to optimize a model or agent.
• ChatGPTused by · ChatGPT is OpenAI’s conversational AI service, which lets users interact with generative AI models and related tools through natural-language prompts.

This explainer was generated by Manning's knowledge graph from the text of Introduction to Generative AI, Second Edition. The section links point to the parts of the book it draws on.