Alignment training

As explained in Reinforcement Learning from Human Feedback

Value alignment is the conformity of an intelligent system's objectives and behavior to the values held by the people or population it is intended to serve.

Alignment training aims to make an intelligent system’s objectives and behavior conform to the values of the people or population it serves. In RLHF, human preferences become an optimizable learning signal rather than a fixed environment reward. section 5.0 section 10.1

Reward models operationalize this goal by compressing complex human judgments into scalar rewards or preference probabilities. The resulting proxy objective guides reinforcement learning, while empirical methods often optimize specific skills rather than directly measuring conformity to values. section 5.0 section 10.1

Alignment training connects several distinct problem settings:

• RLHF learns reward signals from human preference comparisons instead of receiving fixed environment rewards. section 5.0
• Inverse reinforcement learning estimates an agent’s reward function from behavioral trajectories, sharing the high-level alignment problem but differing in implementation and emphasis. section 5.0
• Pluralistic alignment studies how systems can account for values across populations through research, datasets, and personalization. section 10.1

Preference-based reward

Given two candidate answers, a Bradley–Terry reward model predicts the probability that one answer is the preferred, higher-quality response; reinforcement learning can then optimize that learned signal. section 5.0

Figure 5.1 The RM in RLHF plays the role of the environment component that returns rewards in standard RL. The key difference is that in RLHF, we get to control and learn this reward function from human preferences, rather than it being fixed by the environment.

Compare the learned reward model with the fixed environment component in standard reinforcement learning. section 5.0

How Reinforcement Learning from Human Feedback covers it

How deep it goes

Explains reward-model and preference-based mechanisms, but defers detailed data collection and reward-modeling procedures.

Examples

Uses pairwise preferred answers, Bradley–Terry reward models, and learned rewards replacing fixed environment rewards.

What it assumes

Assumes familiarity with reinforcement learning, optimization, human preferences, and modern deep learning systems.

What you can do after

Evaluate/understand how human preferences become proxy rewards and how empirical alignment differs from direct value calibration.

How it connects

Connects RLHF with reward modeling, reinforcement learning, inverse reinforcement learning, human preferences, and pluralistic alignment.

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Also explained in

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  WordPress Power Toolkit: Harness AI to build next-level websitesThe alignment problem is the challenge of ensuring that an AI system’s behavior remains consistent with human values and goals.
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  How Large Language Models WorkThe alignment problem is the challenge of ensuring that an artificial system's goals and methods remain consistent with the goals and values intended by humans.
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  Generative AI in ActionAlignment is the process of shaping an AI model's behavior to conform to intended goals, values, or constraints.
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  Causal AIAI alignment is the task of making an artificial intelligence system's behavior consistent with human goals and preferences.
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  Introduction to Generative AI, Second EditionAlignment is the property of an artificial-intelligence system whose behavior is consistent with intended human goals, values, and constraints.

Compare how 6 books explain alignment training →

Related concepts

• distillationcontrasts with · Knowledge distillation is a training technique in which a student model learns to reproduce or approximate the outputs or behavior of a teacher model.
• area under the curvemeasured by · The area under a curve is the integral of its values over a specified domain and, in classification evaluation, commonly summarizes performance across decision thresholds.
• evaluationcontrasts with · Evaluation is the systematic assessment of a system, result, or approach against relevant criteria, evidence, or objectives.
• behaviormeasured by · Behavior is the externally observable actions, responses, outputs, or effects of a system or object under specified conditions or interactions.

This explainer was generated by Manning's knowledge graph from the text of Reinforcement Learning from Human Feedback. The section links point to the parts of the book it draws on.