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cs.LG2026
Binary Agreement for Efficient Unsupervised Confidence Calibration of Reasoning LLMs
Thomas Zollo, Jimmy Wang, Richard Zemel
Reasoning language models often lack the calibrated confidence needed for reliable deployment, particularly when correctness labels are unavailable and sampling budgets are limited…
cs.LG2024
AExGym: Benchmarks and Environments for Adaptive Experimentation
Jimmy Wang, Ethan Che, Daniel R. Jiang +1
Innovations across science and industry are evaluated using randomized trials (a.k.a. A/B tests). While simple and robust, such static designs are inefficient or infeasible for tes…
cs.LG2024
Optimization-Driven Adaptive Experimentation
Ethan Che, Daniel R. Jiang, Hongseok Namkoong +1
Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization. Tailoring adaptive methods to addr…