A Review of DeepSeek Models
This paper provides an in-depth review of the cutting-edge techniques behind DeepSeek's open-source LLMs—DeepSeek-V3 and DeepSeek-R1. These models achieve state-of-the-art performance with significantly lower resource requirements compared to proprietary counterparts. Key highlights include:
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Multi-Head Latent Attention (MLA) – Introduces efficient attention by compressing keys and values into a latent vector, dramatically reducing memory consumption for long-context tasks without sacrificing performance. MLA employs low-rank compression and decoupled Rotary Position Embeddings, outperforming standard multi-head attention.
Advanced Mixture of Experts (MoE) – Incorporates fine-grained expert segmentation and dedicated shared experts, significantly enhancing combinational flexibility. An innovative load-balancing strategy further optimizes computational efficiency and model performance.
Multi-Token Prediction (MTP) – Enhances training efficiency by predicting multiple subsequent tokens simultaneously. Although effective, the additional training overhead warrants further optimization.
Algorithm-Hardware Co-design – Presents engineering advancements like DualPipe scheduling, an algorithm designed to eliminate pipeline bubbles, and FP8 mixed-precision training, maximizing computational efficiency and reducing training resources.
Group Relative Policy Optimization (GRPO) – Offers a streamlined RL algorithm eliminating value function approximation from PPO, directly estimating advantages from grouped outputs, drastically reducing GPU memory usage.
Post-Training Reinforcement Learning – Demonstrates pure RL's capability in DeepSeek-R1-Zero, which learns advanced reasoning without supervised fine-tuning. DeepSeek-R1 further improves this approach via iterative cold-start fine-tuning, rejection sampling, and RL alignment to enhance reasoning quality and language consistency.