Regularized Greedy Bandits: New Research Shows Simple Strategies Win Finite-Horizon Tests

Researchers from the operations research and machine learning community have released a new working paper, "The Greedy Advantage in Finite-Horizon Bandits," submitted to Management Science. The authors—Kai Zhou, Michael Lingzhi Li, and Kai Wang—tackle a practical gap in multi-armed bandit theory: most algorithms are optimized for long-term asymptotic regret, but real-world experiments often run over short, externally imposed horizons. Their work, available on arXiv (2607.29375), focuses on Bernoulli bandits where each option yields binary rewards, a common scenario in A/B testing and product experimentation.

The paper introduces a family of regularized greedy algorithms that balance exploration and exploitation by adding a penalty term to the standard greedy choice. The authors derive the first finite-horizon regret envelopes for these policies, proving that total regret decomposes into a transient exploration cost plus a suboptimality term that decays exponentially with the regularization strength. This mathematical structure allows for principled calibration of regularization parameters, and as a limiting case, yields sharper guarantees for the classical greedy policy. In extensive numerical experiments, the calibrated regularized greedy methods consistently match or outperform more complex state-of-the-art algorithms, suggesting that simpler approaches can be highly competitive in practical, time-bounded settings.