Direct Preference Optimization, Intuitively Explained

Author(s): Tim Cvetko

Originally published on Towards AI.

The Top Secret Behind Effective LLM Training in 2024

Large-scale unsupervised language models (LMs) have shown remarkable capabilities in understanding and generating human-like text. However, achieving precise control over their behavior poses a significant challenge due to the unsupervised nature of their training. Existing methods rely on collecting human labels to fine-tune LMs, often through reinforcement learning from human feedback (RLHF). Here’s what this article contains:

The Limitations of RLHF — Reinforcement Learning with Human FeedbackThe DPO Architecture & Why It’s So UsefulA 5-Step Guide to Building Your DPO LLMCurrent State of LLM Development

Who is this blog post useful for? ML Engineers(LLM), Tech Enthusiasts, VCs, etc.

How advanced is this post? Anybody previously acquainted with ML terms should be able to follow along.

Replicate my code here: https://github.com/Timothy102/llm-course or through Colab

PPO stands for proximal policy optimization in the context of solving RF problems. The algorithm works by optimizing the reward policy and then fine-tuning the unsupervised LLM according to reward maximization of such human-reinforced preference feedback.

Policy loss is calculated according to the reward policy function.LLM is used to create completions.Small updates to the model are applied and checked.We keep the updates within the “trust” region. Calculate policy loss.Image by: GenAI with LLMs Course

The fundamental principle of PPO centers on implementing gradual, incremental… Read the full blog for free on Medium.

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Published via Towards AI