arxiv:2605.06741

A Closed-Form Upper Bound for Admissible Learning-Rate Steps in Belief-Space Dynamics

Published on May 7

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Abstract

Admissibility in learning-rate steps is characterized by contractivity in KL/Bregman geometry, providing a formulaic upper bound rather than a tunable parameter.

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Learning-rate steps are usually treated as hyperparameters. This paper isolates a local beliefspace calculation: when an update is modeled as a projected forward step on the probability simplex, admissibility means contractivity in the natural KL/Bregman geometry. Under this model, the upper bound of an admissible step is not a tuning slogan but a formula.

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OzTianlu

Paper submitter about 5 hours ago

Learning-rate steps are usually treated as hyperparameters. This paper isolates a local belief-space calculation: when an update is modeled as a projected forward step on the probability simplex, admissibility means contractivity in the natural KL/Bregman geometry. Under this model, the upper bound of an admissible step is not a tuning slogan but a formula.

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