RLHF on AI VOID

Chapter 8: Implementing Basic RLHF Workflows with Tunix

Fri, 30 Jan 2026 00:00:00 +0000

Chapter 8: Implementing Basic RLHF Workflows with Tunix

Welcome back, future LLM maestro! In our journey through Tunix, we’ve explored its architecture, set up our environment, and even fine-tuned models with supervised learning. But what if we want our Language Models (LLMs) to not just predict the next word, but to genuinely understand and align with human preferences? This is where Reinforcement Learning from Human Feedback (RLHF) shines, and Tunix provides the robust, JAX-native tooling to make it happen.

Chapter 12: Advanced RLHF Strategies and Proximal Policy Optimization (PPO)

Fri, 30 Jan 2026 00:00:00 +0000

Introduction

Welcome to Chapter 12! So far, we’ve explored the foundational elements of post-training Large Language Models (LLMs) with Tunix, including supervised fine-tuning and the basics of reward modeling. In this chapter, we’re going to elevate our game by diving into more advanced strategies for Reinforcement Learning from Human Feedback (RLHF), with a particular focus on Proximal Policy Optimization (PPO).

PPO is a cornerstone algorithm in modern RLHF pipelines, enabling robust and efficient alignment of LLMs with human preferences. Understanding PPO is crucial for anyone looking to build highly effective and ethically aligned language models. We’ll break down this powerful algorithm into digestible steps, explore its core mechanics, and demonstrate how Tunix empowers you to implement it for your LLM post-training tasks.

Chapter 14: Project 2: Aligning an LLM for Factual Accuracy

Fri, 30 Jan 2026 00:00:00 +0000

Introduction: Guiding LLMs Towards Truth

Welcome back, future LLM alignment expert! In our previous project, we explored fine-tuning an LLM for a specific style. Now, we’re tackling an even more critical challenge: factual accuracy. Large Language Models, despite their incredible capabilities, are notorious for “hallucinating” – generating plausible-sounding but incorrect information. This can severely limit their trustworthiness and utility in many real-world applications.

In this chapter, we’ll embark on a practical project using Tunix to align an LLM to be more factually accurate. We’ll learn how to leverage Tunix’s powerful post-training framework to reduce hallucinations and ensure our models provide reliable information. This project will reinforce your understanding of data preparation, reward modeling, and iterative alignment techniques.