RLHF Alignment Template

A comprehensive template for aligning Large Language Models (LLMs) using Reinforcement Learning from Human Feedback (RLHF).

Overview

This project provides a full-stack solution for LLM alignment, including:

• Training and fine-tuning with RLHF
• Reward model development
• Interactive web interface for feedback collection
• Model explainability dashboards
• Scalable deployment with Kubernetes

Architecture

├── app/                    # Web interface and API
│   ├── auth.py            # Authentication
│   ├── feedback.py        # User feedback collection
│   └── ui.py              # Chat interface
├── src/
│   ├── preprocessing/     # Data cleaning and tokenization
│   ├── training/          # Fine-tuning and transfer learning
│   ├── reinforcement/     # RLHF implementation
│   └── evaluation/        # Model evaluation metrics
├── dashboards/            # Monitoring and explainability
└── deployment/            # Kubernetes and Docker configs

Key Features

Feature	Description
RLHF Training	Align models with human preferences using reward modeling
Transfer Learning	Leverage pre-trained models (BERT, GPT) for custom tasks
Feedback Loop	Collect user ratings to continuously improve alignment
Explainability	SHAP-based dashboards for model decision transparency
Scalable Deployment	Docker + Kubernetes with auto-scaling (HPA)

Getting Started

Prerequisites

• Python 3.8+
• Docker & Docker Compose
• Kubernetes (optional, for production)

Installation

# Clone the repository
git clone https://github.com/yanfeid/rlhf-alignment-template.git
cd rlhf-alignment-template

# Install dependencies
pip install -r requirements.txt

# Run locally with Docker
docker-compose up --build

Access the application at http://localhost:5000

Training Pipeline

1. Data Preprocessing - Clean and tokenize training data
2. Reward Model - Train a reward model on human preference data
3. RLHF Fine-tuning - Optimize the LLM using PPO with the reward model
4. Evaluation - Measure alignment with human values
5. Feedback Collection - Gather user ratings for continuous improvement

Deployment

# Deploy to Kubernetes
kubectl apply -f deployment/kubernetes/deployment.yml
kubectl apply -f deployment/kubernetes/service.yml
kubectl apply -f deployment/kubernetes/hpa.yml

Monitoring

• Prometheus - Metrics collection
• Grafana - Visualization dashboards
• ELK Stack - Centralized logging

Author

Yanfei Dai

License

MIT License