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Institute for Data, Systems, and Society, School of Business, The Division of Physics, Mathematics and Astronomy, Department of Computing and Mathematical Sciences, School of Industrial and Systems Engineering, Department of Civil and Environmental Engineering, Operations Research Center, Massachusetts Institute of Technology, Purdue University, California Institute of Technology, Georgia Institute of Technology
MIT CSAIL3
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LLMs can be trained to negotiate like expert agents, extracting significantly higher surpluses by strategically exploring buyer markets rather than fixating on immediate bids.
Transformers can effectively mimic Bayesian updating processes to achieve oracle-level efficiency in average treatment effect estimation, outperforming conventional methods.
Stop rewarding all LLM-generated candidates equally: ShapE-GRPO uses Shapley values to fairly distribute credit within sets, leading to better training and faster convergence.
