Sampling dispreferred completions proportionally to the current reward model, as in contrastive divergence, improves preference-optimization performance and is framed as NLL estimation.
Noise contrastive alignment of language models with explicit rewards
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Preference Optimization via Contrastive Divergence: Your Reward Model is Secretly an NLL Estimator
Sampling dispreferred completions proportionally to the current reward model, as in contrastive divergence, improves preference-optimization performance and is framed as NLL estimation.