Reinforcement learning from human feedback (RLHF)

Reinforcement learning from human feedback (RLHF)

Make another page: Human Alignment: Ensures AI systems are better aligned with human values, preferences, and intentions, making them more useful and acceptable to users.

Bias Mitigation: Helps identify and correct biases in AI responses by incorporating diverse human feedback, leading to more fair and equitable AI outcomes.

Improved Adaptability: Enhances the model’s ability to tackle complex, nuanced, or novel tasks by learning from human evaluations and guidance.

Reduced Reward Hacking: Mitigates the risk of models exploiting loopholes to achieve high performance on metrics without genuinely understanding or completing tasks as intended.

Enhanced Safety and Reliability: Increases the safety and reliability of AI systems by continuously refining their behavior based on feedback about what is considered safe and appropriate in human contexts.

Customization and Personalization: Allows for the customization of AI behavior to suit specific user needs or organizational goals, providing more personalized and relevant interactions.

Continuous Learning and Improvement: Facilitates ongoing improvement of AI systems even after deployment, as they can be updated based on new human feedback, adapting to changing norms and user expectations.

Pre-training: The model undergoes initial pre-training on a large dataset to learn the basic structure of language and gain a broad understanding of the world. This step provides the model with a foundation of knowledge.

Feedback Collection: Human evaluators interact with the model by providing feedback on its outputs. This feedback can take various forms, including ratings, corrections, or binary choices between different outputs, reflecting the evaluators’ preferences and values.

Reward Modeling: The collected feedback is used to train a reward model. This model learns to predict the quality or appropriateness of the AI’s outputs based on human evaluations. Essentially, it aims to understand what humans consider good or bad responses.

Policy Optimization: With the reward model in place, the original AI model is further trained using reinforcement learning techniques. In this phase, the model generates outputs that are then evaluated by the reward model, and it adjusts its parameters to maximize the predicted rewards. This step encourages the AI to produce outputs that align more closely with human feedback.

Iteration: The above steps can be repeated multiple times, with the AI model undergoing multiple rounds of feedback collection, reward modeling, and policy optimization. Each iteration aims to further refine the model’s responses to better align with human values and preferences.

Deployment and Continuous Improvement: Once the model achieves a satisfactory level of performance, it can be deployed for use. However, the process doesn’t stop there. Continuous feedback from users can be leveraged to further improve the model, ensuring it remains aligned with user expectations and adapts to new information or changing societal norms.