New Study Establishes Near-Minimax Sample Complexity for Multi-Objective Reinforcement Learning

This story is from 2026-11-06. It is preserved in the archive; the latest stories are on the live feed.

A new arXiv paper shows that in multi-objective reinforcement learning, a single dataset collected under generative models can support a continuum of reward functions with near-minimax sample complexity, even when preferences are specified only after data collection.

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2026-11-06 05:00 · arXiv stat.ML
Near-Minimax Sample Complexity for Multi-Objective Reinforcement Learning under Generative Models

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