Reinforcement learning with human feedback

As explained in Software Testing with Generative AI

A model-alignment technique that uses human feedback to guide or refine a model's behavior.

Reinforcement learning with human feedback (RLHF) fine-tunes model behavior from human usage and feedback, increasing the probability of outputs that fit observed patterns. The technique helps explain why outputs vary, because LLMs remain probabilistic rather than repositories of structured information. section 2.1

Predictive messaging analogy

A phone keyboard may increase the probability of words such as “I am” and “away” because someone types them frequently. This illustrates RLHF, although applying the analogy directly to LLMs is an oversimplification. section 2.1

Figure 2.1 Probability distribution in action

Use the probability-distribution figure to visualize why model outputs are probabilistic and require vigilance. section 2.1

How Software Testing with Generative AI covers it

How deep it goes

Defines RLHF through a predictive-keyboard analogy but stops short of explaining its training algorithm or implementation.

Examples

Uses predictive phone messaging and frequently typed words as its concrete analogy.

What you can do after

Evaluate model outputs as probabilistic and apply vigilance when relying on them.

How it connects

Connects RLHF with large language models, unsupervised learning, and generative pretrained transformers.

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Also explained in

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  Reinforcement Learning from Human FeedbackReinforcement learning from human feedback (RLHF) is a model-alignment technique that optimizes behavior using human preference signals, often through a learned reward model or direct preference objective.
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  LLMs in ProductionReinforcement learning from human feedback is a model-training approach that optimizes behavior using preference or evaluation signals supplied by people.
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  How Large Language Models WorkReinforcement learning from human feedback is a technique that optimizes a model's behavior using human evaluations as a source of reward or preference information.
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  Generative AI in ActionReinforcement learning from human feedback is a model-training technique that uses human preferences as feedback to optimize generated behavior.
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  The AI Pocket BookA training method that uses human judgments as feedback to guide reinforcement learning and improve model outputs.
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  Build a Reasoning Model (From Scratch)A training approach that uses human preference labels to optimize a language model toward responses humans prefer.
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  Grokking AI ApplicationsReinforcement learning from human feedback is a training technique that optimizes model behavior using human evaluations of its responses.
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  Natural Language Processing in Action, Second EditionReinforcement learning with human feedback is a model-training technique that uses human evaluations to optimize a system's behavior.
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  Introduction to Generative AI, Second EditionReinforcement learning from human feedback (RLHF) is a technique that uses human evaluations or preferences to estimate rewards and optimize model behavior.
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  Deep Learning with R, Third EditionReinforcement learning with human feedback is a training approach that optimizes a model using human preference judgments converted into reward signals.
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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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  First-Party Data ActivationReinforcement learning from human feedback is a model-training technique that uses human evaluations to shape a system’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 Software Testing with Generative AI. The section links point to the parts of the book it draws on.