What is the primary focus of the RLHF skill?

The skill provides detailed technical patterns, implementation guides, and best practices for aligning language models with human preferences using RLHF and direct alignment methods.

Does this skill cover DPO and other modern alternatives?

Yes, it includes comprehensive technical comparisons between traditional RL-based optimization (PPO) and direct alignment algorithms like DPO, IPO, and KTO.

How does it help with reward hacking?

The skill outlines mitigation strategies such as implementing strong KL regularization, using reward model ensembles, and establishing robust human evaluation benchmarks.

Who is this RLHF skill designed for?

It is designed for LLM engineers, data scientists, and developers who are fine-tuning models to improve helpfulness, safety, and instructional following.

RLHF Alignment Guide

Name: RLHF Alignment Guide
Author: itsmostafa

byitsmostafa

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Ciencia de Datos y ML

Provides comprehensive technical guidance on Reinforcement Learning from Human Feedback for aligning large language models with human preferences.

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This skill serves as a specialized technical resource for engineers and researchers focused on LLM alignment. It delivers in-depth knowledge on the standard three-stage RLHF pipeline—Supervised Fine-Tuning (SFT), Reward Modeling, and Policy Optimization—as well as modern direct alignment alternatives like DPO. Users can leverage this skill to understand complex concepts like KL regularization, reward hacking mitigation, and preference data collection strategies, making it indispensable for developing helpful, harmless, and honest AI models.

Características Principales

Technical comparisons of direct alignment algorithms including DPO, IPO, and KTO
Comprehensive breakdown of the 3-stage RLHF pipeline (SFT, RM, PPO)
10 GitHub stars
Detailed explanations of mathematical frameworks like the Bradley-Terry model
Best practices for preference data collection and quality management
Strategies for identifying and mitigating over-optimization and reward hacking

Casos de Uso

Designing and implementing reward models for custom domain-specific LLMs
Troubleshooting alignment issues such as model sycophancy or unintended verbosity
Transitioning from complex PPO pipelines to simpler Direct Preference Optimization (DPO)

Acerca de

Características Principales

Technical comparisons of direct alignment algorithms including DPO, IPO, and KTO
Comprehensive breakdown of the 3-stage RLHF pipeline (SFT, RM, PPO)
10 GitHub stars
Detailed explanations of mathematical frameworks like the Bradley-Terry model
Best practices for preference data collection and quality management
Strategies for identifying and mitigating over-optimization and reward hacking

Casos de Uso

Designing and implementing reward models for custom domain-specific LLMs
Troubleshooting alignment issues such as model sycophancy or unintended verbosity
Transitioning from complex PPO pipelines to simpler Direct Preference Optimization (DPO)