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rlhf

Align language models with human preferences via PPO and DPO workflows.

24|Updated Jan 5, 2026
Search Tags:#reinforcement-learning#dpo#rlhf#human-feedback#policy-optimization#preference-data#reward-modeling
One-click install
npx skills add https://github.com/itsmostafa/llm-engineering-skills --skill rlhf
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rlhf
Source: https://github.com/itsmostafa/llm-engineering-skills/tree/main/skills/rlhf
Command: npx skills add https://github.com/itsmostafa/llm-engineering-skills --skill rlhf

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Aligns language models with human preferences to produce safer, more helpful outputs by integrating human feedback into model training and evaluation.

Core Features & Use Cases

  • Preference data collection and labeling workflows
  • Reward modeling for scoring outputs
  • Policy optimization (PPO/DPO) and direct alignment techniques
  • End-to-end RLHF pipelines from SFT to aligned deployment

Quick Start

Train a baseline SFT model, collect human preferences, train a reward model, and run PPO or DPO to obtain an aligned policy.

Frequently Asked Questions about rlhf

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does RLHF align language models with human preferences?▼

RLHF aligns language models with human preferences by collecting preference data, training a reward model to score outputs, and applying policy optimization techniques like PPO or DPO to produce safer, more helpful responses.

What is the difference between PPO and DPO for policy optimization?▼

PPO optimizes a policy against a trained reward model, while DPO skips explicit reward modeling by directly aligning the policy with preference data. This Skill supports both PPO and DPO for direct alignment workflows.

What are the steps to build an end-to-end RLHF pipeline?▼

An end-to-end RLHF pipeline involves training a baseline SFT model, collecting and labeling human preference data, training a reward model, and running PPO or DPO optimization to obtain an aligned policy ready for deployment.

Can I use direct alignment methods without training a separate reward model?▼

Yes, direct alignment methods like DPO allow you to apply preference data directly to policy optimization without training a separate reward model. This Skill supports direct-alignment workflows alongside traditional PPO pipelines.

Do I need supervised fine-tuning before applying human feedback optimization?▼

Yes, you should train a baseline SFT model first. The RLHF workflow progresses from supervised fine-tuning through preference data collection and reward modeling to final PPO or DPO policy optimization.

How do I collect and label preference data for reward modeling?▼

Preference data collection involves gathering human feedback on model outputs to label which responses are preferred. This Skill provides workflows for preference data collection and labeling to support subsequent reward modeling and policy optimization.

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