RLHF (Reinforcement Learning from Human Feedback)

A training technique that aligns models with human preferences using a reward model learned from human ratings.

RLHF is a big reason chatbots feel helpful and polite rather than just predicting likely text. Newer variants like DPO achieve similar alignment with a simpler training setup.

Related resources

Hands-on LLM Fine-Tuning

A practical, hands-on guide to fine-tuning large language models, covering parameter-efficient approaches like LoRA and QLoRA alongside traditional full fine-tuning. It walks through dataset preparation, hyperparameter tuning, and evaluation metrics with real code examples. Aimed at ML engineers who want to adapt base models to specific domains without the cost of training from scratch.