Waseem Alshikh’s Post

A few months ago, we made a strategic decision to shift our focus to a #DPO and #DSS. While our experience with #RLHF was limited, we discovered that it did not make sense for our business despite the hype surrounding it. So, what exactly is DPO and why did we choose it over RLHF? #DPO is like having a recipe book that directly tells you the best dishes to cook based on customer preferences, enabling you to immediately focus on making those dishes better. It’s efficient because it directly aligns your efforts with what’s most preferred, leading to better dishes in less time. On the other hand, #RLHF is like cooking meals, then waiting for customers' feedback to understand what works and what doesn’t. It’s a trial-and-error process where you gradually improve based on varying feedback, which can be time-consuming and less direct. We chose #DPO over #RLHF for several reasons. Firstly, DPO allows our models to learn and adapt directly from specific guidelines (what people like), rather than through trial and error. This directness significantly speeds up the learning process, making our development more efficient. Secondly, by using DPO, we are focusing our efforts on directly optimizing what matters most to our users, enhancing user satisfaction and product quality. Thirdly, DPO uses fewer computational resources compared to the iterative feedback approach of RLHF. Finally, with the direct and efficient approach of DPO, our team can allocate more time to innovate and improve upon the core functionalities of our products, ensuring we remain leaders in our field. Our strategic pivot to DPO aligns perfectly with our commitment to delivering top-notch, user-centered #LLMs. It not only marks a significant improvement in how rapidly and effectively we can respond to user preferences. If you're interested in fine-tuning any of our #Palmyra #LLMs, whether the open sources or the enterprise models, reach out to!