New Algorithm Reduces Large Action Spaces in Bandit Problems

Global: New Algorithm Reduces Large Action Spaces in Bandit Problems

Researchers Quan Zhou, Mark Kozdova, and Shie Mannor released a preprint on arXiv that introduces a method for selecting a representative subset of actions from a large action space shared by a family of bandit problems. The paper, first submitted on May 23, 2025 and revised on January 29, 2026, aims to achieve performance close to that of using the full action set while dramatically shrinking the number of actions that must be considered. The work addresses the need for efficient decision‑making in settings where actions are numerous but rewards exhibit underlying correlations.

Problem Context

In many practical applications—such as recommendation systems, adaptive experimentation, and online advertising—the set of possible actions can be extremely large, making exhaustive exploration computationally infeasible. Existing bandit algorithms typically treat each action independently, ignoring potential relationships among rewards that could be leveraged to reduce the effective dimensionality of the problem.

Algorithm Overview

The authors propose an algorithm that identifies a representative subset of actions by exploiting observed reward correlations without requiring prior knowledge of the correlation structure. The method iteratively samples actions, estimates pairwise similarities, and constructs a reduced action set that preserves the statistical properties of the full space.

Theoretical Guarantees

Formal analysis demonstrates that the algorithm’s regret bounds are comparable to those of standard bandit approaches that operate on the complete action set. Specifically, the authors prove that the loss incurred by using the reduced subset scales sublinearly with the number of rounds, matching the optimal order of magnitude for the original problem under reasonable assumptions about reward correlation.

Empirical Evaluation

Experimental results on synthetic and real‑world datasets show that the proposed method outperforms classic Thompson Sampling and Upper Confidence Bound (UCB) strategies when the action space is large and correlated. In benchmark tests, the algorithm achieved near‑full‑space performance while reducing computational overhead by up to 70 percent.

Implications and Future Work

By enabling efficient action selection in high‑dimensional bandit settings, the approach could broaden the applicability of online learning techniques to domains that were previously limited by scalability concerns. The authors suggest extensions to contextual bandits and to scenarios with non‑stationary reward structures as promising directions for further research.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.