The field of large language models (LLMs) is moving towards more efficient and adaptive methods for fine-tuning and deployment in specialized domains. Recent developments focus on reducing the need for large amounts of labeled data and improving the ability of LLMs to generalize across tasks and domains. Notable advancements include the use of uncertainty signals, iterative amortized inference, and adaptive hierarchical routing to improve the efficiency and effectiveness of LLMs. These innovations have the potential to enable the deployment of LLMs in a wider range of applications and settings. Noteworthy papers include: Synergistic Test-time Adaptation for LLMs, which presents a framework for adapting LLMs on-the-fly without additional supervision, achieving significant gains in performance. Iterative Amortized Inference, which proposes a unified framework for amortized learning and introduces a scalable and extensible approach for general-purpose task adaptation. MeTA-LoRA, which improves data efficiency in multi-task adaptation by leveraging inter-task knowledge and promoting knowledge transfer across tasks. HiLoRA, which achieves substantial improvements in domain generalization through adaptive hierarchical routing over LoRA pools. OPLoRA, which prevents catastrophic forgetting during parameter-efficient fine-tuning by using orthogonal projections to preserve essential pre-trained knowledge. K-Merge, which enables online continual merging of adapters for on-device LLMs, incorporating new LoRAs while preserving performance on previously supported tasks. On-device System of Compositional Multi-tasking, which proposes a novel approach for compositional multi-tasking scenarios involving summarization and translation, enabling effective integration and reduced computational overhead.