The field of reinforcement learning and game theory is rapidly advancing, with a focus on developing more efficient and robust algorithms for complex decision-making problems. Recent research has explored the use of symmetry in Markov games, enabling players to compete without observing payoffs, and the development of novel frameworks for sparse tensor decomposition and nonlinear reinforcement learning. Additionally, there has been a growing interest in harnessing information in incentive design and analyzing the fundamental structure of reward functions to enable efficient sparse-reward learning. Noteworthy papers include: Playing Markov Games Without Observing Payoffs, which introduces a new class of zero-sum symmetric Markov games and shows that players can compete without observing payoffs. ReLATE: Learning Efficient Sparse Encoding for High-Performance Tensor Decomposition, which presents a novel learning-augmented method for automatic construction of efficient sparse tensor representations. The Geometry of Nonlinear Reinforcement Learning, which presents a unified geometric framework for viewing reward maximization, safe exploration, and intrinsic motivation as instances of a single optimization problem.