The field of artificial intelligence is witnessing significant advancements in efficient scaling and compression methods for various models, including Mixture-of-Experts (MoE) models, large language models (LLMs), and neural networks. A common theme among these developments is the focus on reducing computational and memory overhead without sacrificing accuracy.
In the realm of MoE models, innovative techniques such as static quantization, dynamic expert pruning, and expert merging have achieved extreme compression with minimal accuracy loss. Notable papers include MC# and REAP the Experts, which demonstrate the effectiveness of these methods in achieving significant weight reduction and near-lossless compression.
The field of data compression and sensing is also experiencing notable advancements, with tensor-based methods, analog compressed sensing, and learned codecs showing promising results. Standard SVD-based compression approaches have been found effective for spatiotemporal data, while approximate proximal operators can be realized using electric analog circuits. Learned codecs tailored to Earth observation have achieved significant compression ratios, outperforming classical codecs.
Large language models are being optimized for more efficient deployment methods, with techniques such as quantization, pruning, and knowledge distillation being explored. Notable papers include AnyBCQ, ADiP, Bhasha-Rupantarika, and XQuant, which present innovative approaches to enable the deployment of LLMs on resource-constrained devices.
Furthermore, LLMs are being fine-tuned for specialized domains, with a focus on reducing the need for large amounts of labeled data and improving generalization across tasks and domains. Uncertainty signals, iterative amortized inference, and adaptive hierarchical routing are being used to improve the efficiency and effectiveness of LLMs. Noteworthy papers include Synergistic Test-time Adaptation for LLMs, Iterative Amortized Inference, MeTA-LoRA, HiLoRA, OPLoRA, and K-Merge.
The training of large language models is also being optimized, with adaptive fine-tuning strategies such as skill-targeted adaptive training being explored. Calibration data curation is being used to preserve model capabilities after compression, while critical token fine-tuning, dynamic nested depth, and hierarchical alignment are being used to enhance model reasoning and performance.
In addition, pruning techniques are being developed to reduce computational requirements and storage needs for LLMs. Novel pruning frameworks and criteria are being proposed to address the limitations of traditional methods, with notable papers including PermLLM, Don't Be Greedy, Just Relax! Pruning LLMs via Frank-Wolfe, Entropy Meets Importance, and A Free Lunch in LLM Compression.
Finally, neural networks are being compressed using techniques such as progressive depth expansion, low-rank decomposition, and structured sparsity. Combining multiple compression techniques is showing promising results, with notable papers including Vanishing Contributions, Optimally Deep Networks, D-com, and Real-Time Neural Video Compression with Unified Intra and Inter Coding.
Overall, the field of artificial intelligence is witnessing significant advancements in efficient scaling and compression methods, with a focus on reducing computational and memory overhead without sacrificing accuracy. These developments have the potential to enable the widespread adoption of AI models in various applications and settings.
