Direct Preference Optimization: DPO Math & JAX Implementation [Road to Reasoning #8]

We saw how complex RLHF was, both conceptually and in practice. Not only was it hard to implement, but it results in many potentially unstable and expensive training runs. Direct Preference Optimization bypasses all of this, by reparamerizing the reward loss into the policy function, allowing us to collapse all of the PPO objective into a simple classification loss! Join us in implementing this beautiful algorithm from scratch. View my implementation code and the official papers: GitHub: https://github.com/thealepo/llm-reaso... Research Paper(s): https://arxiv.org/abs/2305.18290 LLM REASONING & RL FROM SCRATCH: COMPLETE ROADMAP This video is part of an ongoing, first-principles curriculum tracking the progression of reinforcement learning and alignment for modern reasoning models. Bookmark the playlist to follow the journey from zero to hero! Full Playlist:    • Road to Reasoning: A Journey Through LLM P...