A proposed zeroth-order paradigm for LLM preference alignment addresses likelihood displacement, a problem that motivates alternative ways to extract information from preference pairs with small likelihood margin. The approach targets direct preference alignment methods, which are widely used to align large language models with human preferences because of their computational and memory efficiency.
Direct preference alignment methods are widely used to align large language models (LLMs) with human preferences because of their computational and memory efficiency. However, likelihood displacement motivates alternative ways to extract information from preference pairs with small likelihood margin
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