Reinforcement learning with human feedback

As explained in Grokking AI Applications

Reinforcement learning from human feedback is a training technique that optimizes model behavior using human evaluations of its responses.

Reinforcement learning from human feedback improves a language model by having people judge its responses. The model uses those judgments as a feedback loop, learning through trial and error to produce more effective, human-like conversation. section 1.5

A conversational model develops through three broad stages:

• Unsupervised learning teaches the model to predict coherent word completions. section 1.5
• Supervised refinement trains it on human conversation examples for meaningful question-and-answer exchanges. section 1.5
• Human reviewers identify effective and ineffective responses, guiding later behavior toward better conversations. section 1.5

Figure 1.6 The learning stages of ChatGPT

The learning-stages figure provides a visual overview of how human feedback follows unsupervised learning and supervised refinement. section 1.5

How Grokking AI Applications covers it

How deep it goes

Explains the feedback loop and learning sequence, but stops short of implementation details or optimization mechanics.

Examples

Uses reviewer judgments of chatbot responses and later user feedback as examples of conversational evaluation.

What it assumes

Positions RLHF after basic unsupervised learning and supervised refinement while introducing its purpose through conversational examples.

What you can do after

Evaluate how human judgments guide a language model toward more coherent and relevant dialogue.

How it connects

Connects reinforcement learning with human feedback to unsupervised learning, supervised learning, language models, and conversational AI.

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Also explained in

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  Deep Learning with Python, Third EditionA training setup that uses human preference feedback to build a reward signal for improving a model’s outputs.
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  Transformers in ActionReinforcement learning from human feedback is a model-optimization technique that uses human preference feedback to define or improve a reward signal.
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  Software Testing with Generative AIA model-alignment technique that uses human feedback to guide or refine a model's behavior.

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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 Grokking AI Applications. The section links point to the parts of the book it draws on.