Introduces pessimistic and opportunistic policies for offline dynamic pricing under no price coverage via partial identification from demand monotonicity, with finite-sample regret bounds that recover standard rates when coverage exists.
Jacob Buckman, Carles Gelada, and Marc G Bellemare
2 Pith papers cite this work. Polarity classification is still indexing.
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S^3-R1 generates synthetic multi-hop questions and uses combined intermediate and final rewards to train RL models for retrieval and answering, reporting up to 10% better out-of-domain generalization.
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A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing
Introduces pessimistic and opportunistic policies for offline dynamic pricing under no price coverage via partial identification from demand monotonicity, with finite-sample regret bounds that recover standard rates when coverage exists.
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$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data
S^3-R1 generates synthetic multi-hop questions and uses combined intermediate and final rewards to train RL models for retrieval and answering, reporting up to 10% better out-of-domain generalization.