AI & LLMs9 min read
Direct Preference Optimization (DPO) vs RLHF: Simplifying Alignment for LLMs
DPO eliminates the need for training separate reward models and complex PPO reinforcement loops by deriving the implicit reward directly from binary preference data.
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The Complexity of PPO in RLHF
Standard RLHF with Proximal Policy Optimization (PPO) requires maintaining four neural networks in GPU memory simultaneously.
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