Reinforcement Learning from Human Feedback (RLHF)

Reinforcement Learning from Human Feedback (RLHF) is a training methodology that uses human preference judgments to fine-tune language models so their outputs better align with human values and intent. Rather than relying solely on pre-specified rewards, human annotators rank or compare model outputs, which trains a reward model that in turn guides policy optimization via algorithms such as PPO (Proximal Policy Optimization). This three-stage pipeline — supervised fine-tuning, reward modeling, and RL optimization — underpins the alignment of models like ChatGPT, Claude, and Gemini.

RLHF has become the dominant technique for turning raw pre-trained language models into safe, helpful, and instruction-following assistants, making it a core competency at every major AI lab and an increasingly required skill for ML engineers and researchers working on post-training pipelines. As frontier models shift toward agentic and reasoning workloads, RL-based alignment methods (including RLHF variants, DPO, and RLVR) are consuming a growing share of training compute, driving strong hiring demand. Companies deploying production LLMs need engineers who understand preference data collection, reward modeling pitfalls, and the stability challenges of PPO at scale.