The field of deep learning is moving towards developing more efficient model compression techniques and training methods. Researchers are exploring innovative approaches to reduce the computational costs and memory usage associated with large neural networks. One key direction is the development of pruning techniques that can effectively reduce model size while retaining accuracy. Another area of focus is the improvement of training dynamics, including the use of scaling laws and activation scaling methods. These advances have the potential to significantly improve the performance and efficiency of deep learning models. Noteworthy papers include:

• Towards Universal & Efficient Model Compression via Exponential Torque Pruning, which proposes a novel pruning technique that achieves state-of-the-art compression rates with minimal accuracy loss.
• GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling, which introduces a simple yet effective technique for improving training dynamics.
• Residual Matrix Transformers: Scaling the Size of the Residual Stream, which presents a new transformer architecture that can scale the residual stream independently of compute and model size, leading to improved performance and efficiency.