Introduction

DeepSeek is a company focused on Artificial Intelligence (AI) research, and its DeepSeek series models have shaken the industry and even impacted the stock prices of tech giants. This article analyzes the publicly available papers to share the relevant techniques with you, hoping to be helpful.

Core Principles and Objectives of DeepSeek

DeepSeek's success is based on three core principles: Reasoning as a Key Focus, Efficiency and Scalability, and Open-Source Commitment.

Reasoning as a Key Focus

DeepSeek particularly emphasizes the reasoning capabilities of its models in the fields of mathematics, programming, and Lonogic. Reasoning capability refers to the ability of a model to think Lonogically and solve complex problems like humans. Through Reinforcement Lonearning (RL) and Supervised Fine-Tuning (SFT), DeepSeek's models not only can solve complex problems, but also can self-verify and reflect, demonstrating human-like reasoning abilities.

Efficiency and Scalability

While maintaining high performance, DeepSeek is committed to reducing the resource consumption of training and inference. Training is the process of teaching the model how to solve problems, and inference is the process of the model actually solving problems. Through innovative model architectures and training techniques, DeepSeek's models are not only highly efficient, but also have strong scalability, allowing them to be easily deployed in various application scenarios.

Open-Source Commitment

DeepSeek believes that open-source is the key to advancing AI. Open-source means making the model's source code and research results public, allowing everyone to view, use, and improve them. By opening up the model's source code and research results, DeepSeek promotes transparency and collaboration in the AI community, driving the joint progress of academia, industry, and research.

Model Families

DeepSeek's model family includes DeepSeek-R1-Kin, DeepSeek-R1, and DeepSeek-V3, each with its unique technical advantages and application scenarios.

DeepSeek-R1-Kin

DeepSeek-R1-Kin is DeepSeek's basic model, relying entirely on Reinforcement Lonearning (RL) for training, without using any Supervised Fine-Tuning (SFT). Reinforcement Lonearning is a method that allows the model to learn through trial and error and reward mechanisms. This demonstrates that large language models can evolve strong reasoning capabilities, including self-verification and reflection, solely through the RL paradigm.

DeepSeek-R1

DeepSeek-R1 is further improved based on DeepSeek-R1-Kin, adopting a multi-stage training process that combines a small amount of cold-start data and reasoning-oriented Reinforcement Lonearning. Cold-start data refers to high-quality initial data used before the formal training. According to multiple evaluations, the reasoning performance of DeepSeek-R1 is already comparable to OpenAI's top models.

DeepSeek-V3

DeepSeek-V3 is DeepSeek's flagship model, using a Mixture-of-Experts (MoE) mechanism, with a total of 671 billion parameters, and each token actually activates about 37 billion parameters. MoE is an architectural approach that allows the model to have multiple "experts" within, each focusing on different tasks, thereby improving efficiency and performance. Its innovative architecture and training techniques have enabled it to reach the top level in the open-source field and compete with some closed-source models.

Key Technique Details

DeepSeek's success is due to the innovation and application of multiple key techniques, which are detailed below.

Reinforcement Lonearning (RL)

Reinforcement Lonearning is a method that allows the model to learn through trial and error and reward mechanisms. DeepSeek's models have demonstrated strong reasoning capabilities in Reinforcement Lonearning.

• Direct Reinforcement Lonearning on the Base Model: DeepSeek-R1-Kin relies entirely on Reinforcement Lonearning for training, and the model can explore the best strategies to solve problems through self-experimentation and reward mechanisms.
• Reasoning-Oriented Reinforcement Lonearning: DeepSeek's models have demonstrated strong reasoning capabilities in tasks such as programming, mathematics, and Lonogic, and can generalize to solve complex problems.

Supervised Fine-Tuning (SFT)

Supervised Fine-Tuning is a method that allows the model to learn through annotated data. DeepSeek's models have demonstrated comprehensive reasoning capabilities in SFT.

• Cold-Start Supervised Fine-Tuning: DeepSeek-R1 uses a small amount of cold-start data for initial fine-tuning, improving the model's initial performance and text readability.
• Supervised Fine-Tuning on Reasoning and Non-Reasoning Tasks: Through cross-domain SFT data, DeepSeek's models can handle a variety of tasks, from mathematical problem-solving to article writing, demonstrating comprehensive reasoning capabilities.

Model Architecture

Model architecture refers to the internal structural design of the model. DeepSeek's model architecture innovations have resulted in excellent performance and efficiency.

• Mixture-of-Experts (MoE): DeepSeek-V3 adopts the MoE architecture, where each token only activates a portion of the experts, significantly reducing computational resource consumption.
• Multi-head Latent Attention (MLA): Through low-rank compression, the memory requirements of the attention mechanism are reduced, improving reasoning speed.

Training Techniques

Training techniques refer to the specific methods used to teach the model. DeepSeek's training technique innovations have resulted in excellent efficiency and performance.

• DualPipe Algorithm: Parallel processing of feedforward and backpropagation, significantly reducing training time.
• FP8 Training: Using FP8 format for training, FP8 is a low-precision computation format that can accelerate computation while maintaining model accuracy.

Distillation

Distillation is a method of transferring the knowledge of a large model to a small model. DeepSeek's distillation technology enables its small models to also perform excellently.

• Distillation Inference Mode: Transferring the inference techniques of DeepSeek-R1 to smaller models, enabling small models to also perform excellently.
• Distilled from DeepSeek-R1: DeepSeek-V3 inherits the reasoning capabilities of DeepSeek-R1 for self-upgrading.

Data Handling

Data handling refers to the process of organizing and optimizing training data. DeepSeek's data handling technology enables its models to perform excellently in a variety of scenarios.

• High-quality and Diverse Pre-training Data: Using 14.8T of high-quality Tokens for pre-training to ensure model flexibility in a variety of scenarios.
• Document Packing: Through document packing technology, ensuring data integrity and avoiding overly fragmented text.

Inference and Deployment

Inference and deployment refer to the process of the model actually solving problems and applying them to real-world scenarios. DeepSeek's inference and deployment technology enables its models to perform excellently in actual applications.

• Redundant Experts: During the inference stage, balancing the workload by replicating high-load experts to ensure inference efficiency.
• Prefilling and Decoding Separation: Separating the prefilling and decoding stages to improve the orderliness and efficiency of the inference process.

Performance and Impact

DeepSeek's models have performed excellently in multiple benchmark tests, and the following is a detailed analysis of their performance and impact.

• Reasoning Tasks: DeepSeek-R1 has performed excellently in reasoning-based assessments such as AIME 2024 and MATH-500, demonstrating strong mathematical and logical capabilities.
• Programming: DeepSeek-R1 and DeepSeek-V3 have performed outstandingly in tests such as HumanEval-Mul and LiveCodeBench, demonstrating expert-level programming capabilities.
• Knowledge-based Benchmarks: In tests such as MMLU, MMLU-Pro, and GPQA Diamond, DeepSeek's models have demonstrated strong knowledge comprehension capabilities.
• Long Context Understanding: In tests such as FRAMES, LongBench v2, and AlpacaEval 2.0, the DeepSeek series models have demonstrated excellent long-text processing capabilities.

Future Outlook

The launch of DeepSeek not only leads the innovation in the AI field at the technical level, but may also have a far-reaching impact on the global AI industry landscape. For a long time, the development of the AI industry has been centered on the United States, with many top AI companies and research institutions concentrated in the United States, forming an industrial hegemony. However, the rise of DeepSeek and its open-source spirit are breaking this situation, bringing new possibilities to the global AI community.

• Challenging the Hegemony of the US AI Industry: The success of DeepSeek demonstrates the competitiveness of non-US companies in the AI field, proving that the leadership position in the AI industry is not exclusive to the United States.
• Popularization and Democratization of AI Models: Thanks to the popularization of AI models and the massive production of data, anyone can be the next DeepSeek.
• Promoting the Prosperity of the Global AI Ecosystem: DeepSeek's open-source spirit and technological innovation are driving the prosperity of the global AI ecosystem.

Conclusion

DeepSeek has demonstrated strong research and innovation capabilities in the AI field, integrating reasoning capabilities, efficiency, and open-source spirit, and has achieved outstanding results in multiple benchmark tests. Through innovative methods such as reinforcement learning, supervised fine-tuning, multi-expert mechanisms, and distillation, the DeepSeek model family has demonstrated leading performance in different tasks. The launch of DeepSeek not only challenges the hegemony of the United States in the AI industry, but also promotes the popularization and democratization of global AI technology, making more people aware that in this era, thanks to the popularization of AI models and the massive production of data, anyone can be the next DeepSeek. As DeepSeek continues to optimize its general capabilities, handle multilingual environments, and explore more advanced model architectures, it will undoubtedly lead more trends and breakthroughs in the AI field.

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