Reinforcement Learning with Human Feedback & Direct Preference Optimization

RLHF vs DPO

You can greatly improve your model’s accuracy, reliability, alignment with human expectations and user trust through Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO). So, what’s the difference?

RLHF ensures your LLM data is correct and complete (and, if not, why), for more accurate predictions, less bias and more efficient use of training resources. The most important letter here is H: the human expertise used to evaluate your model’s output that will take it to the next level.

DPO helps adjust your LLM’s tone ensuring it’s just right—whether you need more formality or a more casual feel, longer or shorter answers. Because your users and customers will all have their own response preferences, this process helps you connect with your audience by speaking their language.

To ensure your model meets user expectations and delivers accurate, relevant results, feedback from the right people is key. Whether domain experts for accuracy or a diverse group of contributors to avoid bias, at Defined.ai we connect you with the right crowd for the job.