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Paper detail

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

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.

preprint2026arXivOpen access
Zixi LiYouzhen Li
Machine Learning
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Zixi LiYouzhen Li

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