Curriculum

RLHF

Reinforcement Learning from Human Feedback.

Not started3 min explanation

Visualize, practice, and deep-dive material are optional—use only what helps you learn.

Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

  • Explain the preference-learning pipeline
  • Implement and evaluate a small preference method
  • Design responsible alignment operations

Helpful before starting

  • LLMs and supervised fine-tuning
  • Probability, KL divergence, and optimization
  • Basic reinforcement learning

Start here

RLHF, in plain language

Reinforcement Learning from Human Feedback. Preference optimization adapts model behavior to human goals, but introduces reward misspecification, annotator uncertainty, and difficult safety tradeoffs.

For a small example, one answer is concise and correct; another is polished but wrong. Write a rubric, compare independently, and notice how vague preference labels can reward style over truth. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

  • Demonstrations, pairwise preferences, reward models, policies, and reference models.
  • Bradley-Terry preference likelihood and KL regularization.
  • SFT, PPO-style RLHF, DPO, and their conceptual differences.

Use it well

When RLHF helps—and where it breaks

A base model must follow domain instructions safely. Collect diverse comparisons, train or optimize preferences, hold out evaluators, and test capability regressions and reward hacking. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

  • Treating the reward model as ground truth. Better approach: Use it as a fallible proxy and retain independent evaluation.
  • Discarding annotator disagreement. Better approach: Model or report uncertainty and segment-level preference differences.
  • Optimizing one scalar without guardrails. Better approach: Use a multidimensional evaluation and hard safety constraints.

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