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TabCon is developed, an amortized inference system built on a tabular foundation model that produces confidence intervals for new datasets through a simple forward pass, and offers considerably greater computational efficiency than the classical bootstrap procedure.
BASIS, a critic-free post-training algorithm designed to address the tradeoff between computational efficiency and sample efficiency in value estimation and policy learning, achieves performance close to multi-rollout GRPO-type baselines and often outperforms single-rollout REINFORCE-type baselines.
Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a wide range of scientific and applied domains. As these models proliferate, practitioners often face multiple plausible generators whose performance can vary with the tas...
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