RLHF
Reinforcement Learning from Human Feedback — a training technique where humans rate model outputs to teach the model which responses are preferred, dramatically improving instruction-following and safety.
RLHF is the step that turned GPT-3-class models into ChatGPT-class assistants. The pipeline: collect prompts, generate multiple responses, have humans rank them, train a reward model on the rankings, then use the reward model to fine-tune the LLM via reinforcement learning. The result is a model aligned to human preferences.
Every frontier-tier model (GPT, Claude, Gemini, Llama-Instruct) goes through RLHF or its variants. In 2026, the trend has shifted toward simpler alternatives like DPO (Direct Preference Optimization) and constitutional AI, which achieve similar quality without the complexity of full RL.
For practitioners, RLHF is not something you usually run yourself — it requires large preference datasets and significant compute. You consume the results when you use an instruction-tuned model.
Frequently asked
Is RLHF still used in 2026?+
Yes by frontier labs, but increasingly alongside or replaced by DPO and constitutional AI. The end result (preference-aligned model) is similar; the methods are evolving toward simpler, cheaper alternatives.
Do I need to do RLHF for my own model?+
Almost certainly not. RLHF requires preference data and significant compute. For most teams, start with an instruction-tuned base model, then add LoRA fine-tuning or prompt engineering on top.