rlhf
Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
npx skills add majiayu000/claude-skill-registry --skill rlhf-itsmostafa-llm-engineering-skil-3 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Understanding RLHF Reinforcement Learning from Human Feedback (RLHF) is a technique for aligning language models with human preferences. Rather than relying solely on next-token prediction, RLHF uses human judgment to guide model behavior toward helpful, harmless, and honest outputs. ## Table of Contents - [Core Concepts](#core-concepts) - [The RLHF Pipeline](#the-rlhf-pipeline) - [Preference Data](#preference-data) - [Instruction Tuning](#instruction-tuning) - [Reward Modeling](#reward-modeling) - [Policy Optimization](#policy-optimization) - [Direct Alignment Algorithms](#direct-alignment-algorithms) - [Challenges](#challenges) - [Best Practices](#best-practices) - [References](#references) ## Core Concepts ### Why RLHF? Pretraining produces models that predict likely text, not necessarily *good* text. A model trained on internet data learns to complete text in ways that reflect its training distribution—including toxic, unhelpful, or dishonest patterns. RLHF addresses this gap by optimizing for human preferences rather than likelihood. The core insight: humans can often recognize good outputs more easily than they can specify what makes an output good. RLHF exploits this by co
- Table of Contents
- Core Concepts
- Why RLHF?
- The Alignment Problem
- Key Components
- The RLHF Pipeline
- Stage 1: Supervised Fine-Tuning (SFT)
- Stage 2: Reward Model Training
- Stage 3: Policy Optimization
- Alternative: Direct Alignment
- Preference Data
- Pairwise Preferences
- Collection Methods
- Data Quality Considerations
What does the rlhf skill do?
Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rlhf-itsmostafa-llm-engineering-skil-3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.