RLHF: Unlocking Human-Aligned Models

Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) are key techniques for refining generative AI models, especially in multimodal contexts. These methods leverage human feedback to directly shape model outputs, creating more responsive and human-aligned systems. These methods are typically vital in creating performant generative models.

Thanks to our highly efficient system and expansive network of expert annotators, we deliver feedback in a matter of seconds. This paradigm shift makes it feasible to leverage feedback from Rapidata to be used and inform the loss directly in the training loop.

With Rapidata, you can tap into a worldwide pool of annotators to ensure your models are informed by a diverse array of preferences and perspectives. Our global network and smart targeting facilitate a wide range of cultural, linguistic, and contextual nuances, allowing you to avoid biases and align training with your needs.

Our API enables smooth integration of human feedback into your model’s training pipeline. By embedding real-time feedback directly into the training loop, you can continuously improve the model’s performance based on the most recent and applicable model parameters.