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Groupwise Distortion Guarantees for Preference-Based Alignment

Brodkey, Jacob et al.
arXiv (All) · Papers
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machine-learning
Preference-based alignment methods such as reinforcement learning from human feedback (RLHF) and Nash learning from human feedback (NLHF) aggregate pairwise preferences to learn an LLM policy, but a natural goal is maximizing social welfare (average cardinal utility), which comparisons alone need no
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