The field of natural language processing is moving towards more efficient and effective multi-task learning methods, with a focus on adapting pre-trained models to various downstream tasks. Recent developments have introduced novel approaches to address common challenges such as task interference and negative transfer. These methods enable more flexible and scalable learning frameworks, allowing for better transferability and stability across tasks. Additionally, there have been significant advancements in dialog systems, particularly in the area of dynamic exploration strategies and cognitive dual-systems. These innovations have led to improved performance, efficiency, and generalization in task-oriented dialog systems. Noteworthy papers include: Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation, which introduces a novel parameter-efficient approach for multi-task learning, and DyBBT: Dynamic Balance via Bandit inspired Targeting for Dialog Policy with Cognitive Dual-Systems, which proposes a bandit-inspired meta-controller for dynamic exploration in dialog systems.