Innovative Techniques of DeepSeek Models 🚀
Published: March 25, 2025
Author: Ahmet Onur Durahim
5 min read
In their new paper, Wang and Kantarcioglu examine how the open-source DeepSeek-V3 and DeepSeek-R1 models achieve high performance with fewer resources while competing with proprietary models like GPT or Claude.
🔍 Highlights
🔸 Improvements in Transformer Architecture
Multi-Head Latent Attention (MLA): A new approach that optimizes the attention mechanism.
Mixture of Experts (MoE): Increased efficiency with expert segmentation and shared expert isolation.
🔸 More Efficient Training
Multi-Token Prediction (MTP): Enables prediction of multiple tokens simultaneously, improving training efficiency.
🔸 Engineering Designs
DualPipe: A parallelization algorithm overlapping computation and communication.
FP8 Mixed Precision Training: Enhances computational efficiency using FP8 precision.
🔸 Advanced Reinforcement Learning 🤖
Group Relative Policy Optimization (GRPO): Reduces memory usage and maximizes success through alternating SFT and RL training.