stat.MLOct 11, 2026
SaveNotes on Uniform Policy Score Bounds and Lipschitz Continuity for Truncated Gaussian, Product Cauchy, Boltzmann, and Softmax Policies
Organizations: Xi’an Jiaotong University
Abstract
We establish uniform score bounds and Lipschitz continuity for truncated Gaussian, Cauchy, Boltzmann, and softmax policies under explicitly stated parameterizations.
Figures & tables
| Policy | Fixed structure / uniform bound | ||
|---|---|---|---|
| Truncated Gaussian | , ; fixed; support diameter | ||
| Product Cauchy | , ; scales | ||
| Boltzmann | Linear preference ; ; finite positive carrier mass | ||
| Softmax | linear logits ; ; finite action set |
Table 1: Uniform nuclear-score bounds and operator-to-nuclear score Lipschitz constants. Temperatures, scales, covariance, features, and supports are independent of the learned parameter. All constants are sufficient; only the specific sharpness statements in the text are claimed.
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Policy Learning
Policy learning develops decision rules that choose actions or interventions to maximize outcomes from data, feedback, or interaction.
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