RLHF (Reinforcement Learning from Human Feedback) is a technique that incorporates human judgments into the training loop of reinforcement learning models. Instead of using only automated reward signals, RLHF involves having humans provide guidance—such as ranking outputs or offering corrective examples—to shape the model’s behavior. By aligning the model’s decision-making with human preferences and values, RLHF can produce AI systems that are more intuitive, user-friendly, and aligned with real-world expectations.
How It Works:
- Human Feedback Collection: Humans review model outputs and offer evaluative signals—such as “better” or “worse”—to guide the model.
- Policy Adjustment: The model updates its policy based on both the environment’s rewards and the human-provided feedback.
- Iterative Refinement: Through repeated rounds of evaluation, the model learns to produce responses that better align with human goals and standards.
Why It Matters:
RLHF bridges the gap between purely algorithmic optimization and real-world utility. By incorporating human insights into the training process, it helps ensure that AI systems behave responsibly, ethically, and in ways that resonate with human values. This approach can improve user trust, reduce harmful behaviors, and create more meaningful human–AI collaborations.
