Tianlong Nan
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126
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ML×1AI×1
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2026
Efficient Exploration for Iterative Nash Preference Optimization
The paper proposes a novel, explicitly exploratory iterative Nash Learning from Human Feedback (NLHF) algorithm that achieves strong regret bounds for optimizing LLMs based on complex, non-scalar human preferences.
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