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The primary reason Direct Policy Optimization (DPO) is considered more sample-efficient than Proximal Policy Optimization (PPO) is that DPO requires actively collecting new preference data from an online environment throughout its training process.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
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Choosing an Alignment Method for a Resource-Constrained Project
Which of the following best analyzes the primary reason why Direct Policy Optimization (DPO) is considered more sample-efficient than Proximal Policy Optimization (PPO) for aligning language models?
The primary reason Direct Policy Optimization (DPO) is considered more sample-efficient than Proximal Policy Optimization (PPO) is that DPO requires actively collecting new preference data from an online environment throughout its training process.