SPIN Unprocessed October 15, 2026 ai_technology research
Who Should We Listen to More? Welfare-Aware Preference Acquisition for Pluralistic Alignment
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arXiv:2610.19035v1 Announce Type: new Abstract: Pluralistic alignment needs to accommodate diverse preferences under limited human feedback. Using AI simulators to label preference comparisons reduces annotation costs, but variation in simulation accuracy across individuals can leave some people's preferences poorly represented in training. We study how to allocate human feedback across people to construct training data for pluralistic alignment, and how this allocation affects the trained polic
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