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Direct Policy Optimization (DPO) is considered more sample-efficient than Proximal Policy Optimization (PPO). This efficiency stems from DPO's ability to learn directly from a static, fixed dataset of preferences. In contrast, PPO requires a computationally expensive online sampling process to gather data during training.

Comparison of DPO and PPO Sample Efficiency

Given the lab's constraints described in the case study, which of the following two alignment strategies would be more suitable, and why? 

*   **Strategy A:** An approach that requires iteratively sampling new responses from the model and updating the model in an online loop. 
*   **Strategy B:** An approach that can directly learn from the existing, static dataset of preferences without needing to generate new samples during training.

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.

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