The field of reinforcement learning is moving towards more robust and efficient methods, with a focus on addressing the challenges of high-dimensional state spaces and uncertain environment dynamics. Researchers are exploring new approaches to improve state space coverage, such as distributionally robust auto-encoding, and to develop more effective methods for robust policy learning. Additionally, there is a growing interest in optimal transport and its applications to reinforcement learning, including the development of more efficient and scalable algorithms for computing transport plans. Noteworthy papers in this area include: Imagine Beyond, which proposes a novel method for distributionally robust auto-encoding to improve state space coverage in goal-conditioned reinforcement learning. Linear Mixture Distributionally Robust Markov Decision Processes, which introduces a new framework for distributionally robust Markov decision processes and provides a more refined representation of uncertainties. Differentiable Generalized Sliced Wasserstein Plans, which proposes a differentiable approximation scheme for efficiently identifying the optimal slice in high-dimensional settings. Hybrid Cross-domain Robust Reinforcement Learning, which introduces a hybrid framework for robust reinforcement learning that utilizes an online simulator to complement limited offline datasets. Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data, which proposes a method for flow matching that models the target dynamics as a conditional flow built upon the output distribution of the source-domain flow. Beyond Optimal Transport: Model-Aligned Coupling for Flow Matching, which proposes an effective method for model-aligned coupling that matches training couplings based on geometric distance and alignment with the model's preferred transport directions.