NEAR AI Paper Club DeepSeek V3 & R1 - Architecture & Training Insights

The video introduces DeepSEA, a state-of-the-art model that outperforms many others and is cost-efficient to train. The model architecture and hardware used are briefly discussed, highlighting the performance achieved through data preparation and unique hardware configurations.

Details about multi-head latent attention in the paper DeepSEA are explored. The concept of keys, queries, and positional embeddings in memory for future tokens is discussed. The approach of projecting keys and values into smaller dimensions is explained, along with the strategy to handle consecutive tokens efficiently using projections.

The video delves into the concept of a mixture of experts, a component of the model that utilizes multiple experts to enhance performance. It explains how different matrices are used for experts and how the model learns to efficiently use parameters for improved results.

The complexity of distributed training is discussed, focusing on strategies like data parallelism and expert parallelism. The challenges of balancing computation and communication overhead in distributed training are highlighted, along with insights into optimizing the training process.

An overview of quantization techniques used to optimize training efficiency is provided. The process of reducing precision for computations while maintaining accuracy is explained, showcasing methods to quantize models effectively for improved performance and resource utilization.

The implementation of policy optimization and reinforcement learning in the training process is explored. The video discusses the use of reinforcement learning for tasks like math and programming problems, emphasizing the model's ability to adapt and improve through iterative training cycles.