The article outlines a method for Direct Nash Optimization using reinforcement learning from human feedback (RLHF) to enhance model preferences. It details algorithm derivation, theoretical analysis, and proposes a practical algorithm—Iterative Contrastive Self-Improvement. The paper elaborates on experiments conducted, including a cost analysis of extensive training iterations, sampling outputs, and annotation efforts. The use of batched prompting with GPT-4 for efficient preference annotation plays a crucial role in optimizing the training process for developing efficient response models.
The cost of running the experiment highlighted significant expenses for sampling outputs and annotating them, with a total estimated cost of around $40,000.
Batched prompting was utilized to improve efficiency in gathering preference annotations from GPT-4, allowing simultaneous analysis of multiple candidate responses.
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