Reward signal

As explained in Introduction to Generative AI, Second Edition

A reward is a numerical feedback value representing progress toward a task and reinforcing a model behavior.

A reward signal is a numerical feedback value indicating progress toward a task. In reinforcement learning, rewarded behavior becomes more likely to recur, while penalized behavior becomes less likely. section 1.1

Unlike supervised learning, which supplies desired outputs directly, reinforcement learning uses trial and error. The model generates possible responses and learns which ones receive higher scores. A reward function turns judgments about quality into those scores. section 2.10 section 2.12

For language models, reward signals can come from different evaluators:

• Human feedback uses ratings or preferences to estimate a reward function, as in reinforcement learning from human feedback (RLHF). section 2.10
• AI feedback uses model-generated judgments to train a reward model or verifier, as in reinforcement learning from AI feedback (RLAIF). section 2.12
• Neither source of feedback guarantees a perfect definition of quality: people may disagree, while automated evaluation can increasingly replace human checks without eliminating uncertainty. section 2.10 section 2.12

Figure 1.1 The reinforcement learning cycle

Use the reinforcement learning cycle to connect environmental feedback, rewards or penalties, and changing behavior. section 1.1

How Introduction to Generative AI, Second Edition covers it

How deep it goes

Defines numerical feedback and explains its behavioral effect, but stops short of implementation details or formal optimization.

Examples

Uses model completions, human preference judgments, AI-rated responses, and the reinforcement learning cycle as teaching examples.

What it assumes

Assumes only a basic distinction between supervised learning and reinforcement learning, introducing rewards, penalties, and reward functions from first principles.

What you can do after

Evaluate how rewards shape model behavior and distinguish human-judged from AI-judged alignment feedback.

How it connects

Connects reinforcement learning with reward functions, human feedback, AI feedback, reward models, and supervised learning.

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Also explained in

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  Optimization Algorithms: AI techniques for design, planning, and control problemsA numerical feedback signal indicating the immediate value of an agent's action in a state.
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  Grokking AI Algorithms, Second EditionA score is a numerical evaluation assigned to an output or state to indicate its favorability according to an evaluator.
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  Build a Robo-Advisor with Python (From Scratch)A reward is a numerical feedback signal representing the benefit or cost an agent receives after taking an action.
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  Reinforcement Learning from Human FeedbackA reward is a scalar evaluative signal indicating the desirability of an action or outcome for an agent.
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  Causal AIA reward is a scalar feedback signal that evaluates the desirability of an agent's outcome or action in reinforcement learning.
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  Evolutionary Deep Learning: Genetic algorithms and neural networksA reward is a numerical feedback signal returned by a reinforcement-learning environment after an agent takes an action.
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  Experimentation for EngineersA reward is the observed outcome assigned to a contextual-bandit action, represented here by the user's viewing time.
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  Transformers in ActionA reward signal is a numerical feedback signal that indicates the desirability of an action, output, or state in reinforcement learning.
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  Deep Learning with Python, Third EditionFeedback value used in reinforcement learning to indicate how desirable an action or output is.
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  LLMs in ProductionA reward system is a mechanism that assigns feedback signals to actions or outcomes to guide an agent's behavior.

Compare how 11 books explain reward signal →

Related concepts

• reinforcement learningused by · 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.
• regretmeasured by · Regret is the difference between the reward or utility obtained from a choice and that obtainable from the best alternative under the same circumstances, often accumulated across decisions.
• reward modelproduced by · 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.
• environmentproduced by · An environment is the external context, including its entities and conditions, in which a system, process, or agent exists or operates.
• actionproduced by · An action is a choice or operation selected or performed by an actor that can affect a system or environment or produce an outcome.

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.