The rlhf-from-scratch repository, archived on January 26, 2026, offers a comprehensive educational resource for understanding Reinforcement Learning with Human Feedback in Large Language Models. The project provides both theoretical foundations and practical implementations through a Jupyter notebook tutorial and minimal Python code examples.

At its core, the repository implements a simple PPO training loop (src/ppo/ppo_trainer.py) alongside helper utilities for rollout processing, advantage computation, and reward modeling. The accompanying tutorial walks through the complete RLHF pipeline, from preference data collection to policy optimization, with small runnable experiments that demonstrate key concepts. The codebase prioritizes readability and educational value over production readiness.

This resource serves as an accessible entry point for developers and researchers looking to understand RLHF mechanics without diving into complex production systems. While the repository is now read-only, the tutorial notebook remains available on Google Colab, allowing users to interactively explore the concepts and experiment with the implementations. The project represents a valuable contribution to the ML education ecosystem, bridging the gap between theoretical understanding and practical implementation.