Bandit algorithms with logarithmic regret are not always reliable in real-world scenarios due to statistical inconsistencies. However, new distribution-oblivious algorithms have been developed that offer consistent learning performance, even if the regret is slightly higher than logarithmic. This trade-off between regret and statistical robustness allows for more reliable decision-making in complex environments.