"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis cover image

The Evolution of AI Agents: Lessons from 2024, with MultiOn CEO Div Garg

"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

CHAPTER

Direct Preference Optimization in AI

This chapter explores the Direct Preference Optimization (DPO) algorithm, contrasting it with traditional supervised fine-tuning methods. The discussion focuses on how DPO utilizes both positive and negative feedback to refine model predictions and includes insights on reinforcement learning algorithms like Proximal Policy Optimization (PPO). Additionally, the chapter highlights the use of scoring in AI, emphasizing the role of trajectory-level scoring and the balance between exploration and exploitation in agent decision-making.

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