The field of deep reinforcement learning is moving towards more efficient and effective methods for training agents. One of the key directions is the development of techniques that improve the sample efficiency of deep reinforcement learning algorithms. This includes novel approaches to compressing the policy parameter space, improving the optimization landscape of the critic network, and leveraging large language models for intelligent coordination of multi-robot systems. Another area of focus is the development of methods that enable zero-shot reinforcement learning, where agents can learn to optimize any reward function at test time without requiring explicit training on that task. Noteworthy papers in this area include: From Parameters to Behavior, which develops a novel approach to compressing the policy parameter space, and XQC, which introduces a well-motivated, sample-efficient deep actor-critic algorithm. TD-JEPA is also notable for its introduction of latent-predictive representations for zero-shot reinforcement learning. CaRe-BN is another significant contribution, which proposes a confidence-adaptive and re-calibration batch normalization method for stabilizing spiking neural networks in reinforcement learning. LLM-MCoX is also worth mentioning, which leverages large language models for intelligent coordination of multi-robot systems.