The recent advancements in the field of large language models (LLMs) have been significantly shaped by the development and optimization of Mixture of Experts (MoE) architectures. These models, which leverage specialized experts to handle different tasks, have shown remarkable efficiency and performance improvements over traditional monolithic models. The focus has shifted towards methods that not only enhance the model's capability but also address the resource constraints associated with deploying such large-scale models. Innovations in model merging techniques, such as the distribution-based approaches, have been particularly impactful, allowing for the preservation of specialized capabilities while enabling efficient knowledge sharing. Additionally, the introduction of comprehensive benchmarking frameworks has standardized the evaluation of MoE algorithms, making these complex models more accessible to a broader research community. Furthermore, advancements in model compression techniques have been crucial in reducing the computational and storage costs, thereby making these powerful models more practical for real-world applications. The release of open-source, large-scale MoE models has also played a pivotal role in democratizing access to cutting-edge research, fostering further innovation and application development in the field.

Noteworthy papers include:

• A novel distribution-based approach for merging LLMs that significantly outperforms existing techniques.
• A comprehensive library for benchmarking MoE algorithms, standardizing training and evaluation pipelines.
• A two-stage compression method for MoE models that reduces model size and enhances inference efficiency while maintaining performance.