The field of bandit algorithms and game theory is witnessing significant developments, with a focus on improving regret bounds, adapting to non-stationary environments, and solving complex problems. Researchers are exploring new approaches to achieve optimal performance in various settings, including delayed feedback, non-stationary reward environments, and heavy-tailed rewards. The use of variance-aware adaptation, probabilistic factorial experimental design, and cautious optimism are some of the innovative techniques being employed to advance the field. Notably, the introduction of new algorithms and frameworks, such as RAVEN-UCB and Cautious Optimism, are providing state-of-the-art regret minimization guarantees and improved performance in general games. Noteworthy papers include: Improved Best-of-Both-Worlds Regret for Bandits with Delayed Feedback, which presents a new algorithm that matches the known lower bounds in both stochastic and adversarial regimes. Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms, which proposes a novel policy to learn reward environments over a continuous space using Gaussian interpolation. From Theory to Practice with RAVEN-UCB: Addressing Non-Stationarity in Multi-Armed Bandits through Variance Adaptation, which introduces a novel algorithm that achieves tighter regret bounds than UCB1 and UCB-V. Cautious Optimism: A Meta-Algorithm for Near-Constant Regret in General Games, which demonstrates a framework for substantially faster regularized learning in general games.