Simple Preference Optimization (SimPO): Mathematical Foundations, Reference-Free Implicit Reward, Length Normalization, and Target Margin Dynamics
Simple Preference Optimization (SimPO): Mathematical Foundations, Reference-Free Implicit Reward, Length Normalization, and Target Margin Dynamics Post-training preference alignment has become the definitive step in transforming raw pretrained large language models into instruction-following assistants. While Reinforcement Learning from Human Feedback (RLHF) via Proximal Policy Optimization (PPO) established the initial standard, its requirement to maintain actor, critic, reference, and reward
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