The work establishes a regret lower bound of Ω(T^{2/3} min(K,D)^{1/3}) for fair multi-user dueling bandits with heterogeneous Condorcet winners and gives algorithms achieving matching upper bounds up to logs.
Econometrica , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
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RLVR training teaches a 30B LLM to strategically explore a multi-buyer market and extract 70% of available surplus, outperforming frontier models up to 1T parameters in concurrent negotiation.
NASH decomposes the validation utility into Shapley-informative component functions and aggregates them non-linearly to make Data Shapley-based data selection consistently effective.
citing papers explorer
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Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare
The work establishes a regret lower bound of Ω(T^{2/3} min(K,D)^{1/3}) for fair multi-user dueling bandits with heterogeneous Condorcet winners and gives algorithms achieving matching upper bounds up to logs.
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Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations
RLVR training teaches a 30B LLM to strategically explore a multi-buyer market and extract 70% of available surplus, outperforming frontier models up to 1T parameters in concurrent negotiation.
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Is Data Shapley Not Better than Random in Data Selection? Ask NASH
NASH decomposes the validation utility into Shapley-informative component functions and aggregates them non-linearly to make Data Shapley-based data selection consistently effective.