Preference Tuning and Alignment

Preference tuning is the process of aligning language models with human values and desired behaviors through preference data. Rather than training on raw text alone, preference tuning uses human feedback (or synthetic preferences) to steer model outputs toward better, safer, and more helpful completions. This series covers the full pipeline: from collecting preference pairs and training reward models, through modern alignment methods like RLHF and DPO, to evaluating alignment success and guarding against common failure modes like reward hacking and over-refusal.

Learning preference tuning is essential for practitioners building production LLM applications. Public base models often generate harmful, inaccurate, or unhelpful content without alignment training. By mastering these techniques, you can fine-tune models to match your domain, values, and compliance requirements. Whether you're aligning a coding assistant to prioritize correctness, a customer-service bot to refuse harmful requests, or a research tool to provide balanced perspectives, this series equips you with both theoretical understanding and practical implementation skills.

The series progresses from foundational concepts (what is RLHF and why it matters) through hands-on methods (building preference pairs, training reward models) and advanced techniques (DPO variants, constitutional AI, advanced evaluation). By the end, you'll understand the full spectrum of modern alignment approaches, their tradeoffs, and how to implement them.

Articles in this series​

Articles in this series