RLHF (Reinforcement Learning from Human Feedback)

RLHF is a multi-stage alignment process that bridges raw language model capability and practical usefulness. After a base model is pretrained on large text corpora, human annotators compare pairs of model outputs and label which response they prefer. These preference signals train a separate **reward model** that learns to predict human ratings for any given output. The original language model is then fine-tuned using **Proximal Policy Optimization (PPO)** or a similar RL algorithm, optimizing its outputs to maximize the reward model's score while staying close to its original distribution via a KL-divergence penalty.

The technique was popularized by OpenAI's InstructGPT paper (2022) and underpins the behavior of ChatGPT, Claude, Gemini, and most production-grade assistants. Without RLHF, base LLMs tend to complete prompts in statistically likely ways rather than follow instructions, admit uncertainty, or decline harmful requests. RLHF is also the foundation for more recent variants: **RLAIF** (replacing human raters with another AI), **DPO** (Direct Preference Optimization, which removes the explicit reward model), and **Constitutional AI**, which uses a set of written principles to generate synthetic preference data at scale.

For teams building agent systems, RLHF matters because it shapes the default behavioral tendencies of the foundation models your agents run on — including how they handle ambiguous instructions, tool calls, and multi-step reasoning chains. Understanding RLHF also helps diagnose failure modes: models trained with RLHF can be **reward-hacked**, producing responses that score well on the reward model but are subtly incorrect or sycophantic. Evaluating agents against your own preference criteria — rather than relying solely on RLHF-trained defaults — is often necessary for production deployments.