RLHF Explained: How AI Models Learn Human Preferences

How do AI models learn to follow human intent?

In this video, we break down the alignment stack behind modern large language models, including Reward Modeling, Reinforcement Learning from Human Feedback, and RLHF pipelines.

You will learn how models move from supervised fine-tuning to preference-based training, how reward models are built using pairwise human feedback, and why the KL penalty is critical for preventing reward hacking.

We also explore modern alignment methods like Direct Preference Optimization and Group Relative Policy Optimization, which are becoming popular alternatives to traditional RLHF.

Topics covered:

↳ What RLHF means
↳ How reward modeling works
↳ Pairwise preference data
↳ Bradley-Terry reward modeling
↳ PPO in RLHF pipelines
↳ KL penalty and reward hacking
↳ DPO vs RLHF
↳ GRPO for efficient alignment
↳ Hugging Face TRL for implementation
↳ Why alignment matters for AI safety and behavior

This is a practical AI engineering explanation for anyone learning LLM training, AI alignment, reinforcement learning, and production-grade AI systems.

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Видео RLHF Explained: How AI Models Learn Human Preferences канала Engineering Insider