As AI technology advances, the race to create AI innovations, especially in the field of generative AI, has intensified, resulting in promises as well as concerns.
While these technologies hold the potential for transformative outcomes, there are also associated risks. The development of Reinforcement Learning from Human Feedback (RLHF) represents a significant breakthrough in ensuring that AI models align with human values, delivering helpful, honest and harmless responses. Given the concerns about the speed and scope of the deployment of generative AI, it is now more important than ever to incorporate an ongoing, efficient human feedback loop.
Reinforcement learning from human feedback is a machine-learning approach that leverages a combination of human feedback and reinforcement learning to train AI models. Reinforcement learning involves training an AI model to learn through trial and error, where the model is rewarded for making correct decisions and penalized for making incorrect ones.
However, reinforcement learning has its own limitations. For instance, defining a reward function that captures all aspects of human preferences and values may be challenging, making it difficult to ensure that the model aligns with human values. RLHF addresses this challenge by integrating human feedback into the training process, making aligning the model with human values more effective. By providing feedback on the model’s output, humans can help the model learn faster and more accurately, reducing the risk of harmful errors. For instance, when humans provide feedback on the model’s output, they can identify cases where the model provides inappropriate, biased, or toxic responses and provide corrective feedback to help the model learn.
To learn more – https://www.leewayhertz.com/reinforcement-learning-from-human-feedback/
Michael Scott Blog 