Get the latest tech news
Direct Preference Optimization vs. RLHF
: A Technical Deep Dive We're excited to announce that the Together Fine-Tuning Platform now supports Direct Preference Optimization (DPO)! This technique allows developers to align language models with human preferences creating more helpful, accurate, and tailored AI assistants. In this deep-dive blogpost, we provide details of what DPO is, how it works, when to use it and code examples.
Rather than simply maximizing preferred dishes and minimizing disliked ones in absolute terms, this relative approach ensures you don't completely abandon your fundamental cooking techniques while developing improvements. While DPO directly uses customer preferences to modify your existing recipes, RLHF introduces this third-party critic who guides your experimentation through ongoing taste tests and ratings. Use CaseWhy DPO Works Well Chatbot Responses If your application involves conversations in specific domains like psychology, medicine, or role-playing, then DPO provides a significant enhancement to conversation quality; this helps optimize for engagement and helpfulness Summarization Humans can easily compare summaries, but writing perfect ones is harder Code Generation Different coding styles can be valid; judgments about readability and maintainability are subjective Question Answering Multiple valid approaches with varying levels of helpfulness and clarity Writing Assistance Writing quality is subjective and context-dependent
Or read this on Hacker News
