This is how DeepSeek R1 can challenge OpenAI and other big companies at 1/30 of the cost.

DeepSeek provides an industry-leading inference model: R1 , and at a very low cost - only 1/30 the cost of its main competitor OpenAI's GPT-4 Turbo (o1) !

The success of DeepSeek R1 is inseparable from several key technological breakthroughs:

? 1. Significantly reduce training costs: optimize data and model architecture

The training cost of traditional LLM (Large Language Model) is extremely high, but DeepSeek R1 greatly reduces the cost through more efficient data screening, Mixture of Experts (MoE) structure, and optimized computing efficiency .

• MoE (Mixed of Experts) architecture : Traditional large models call the entire neural network when processing each input, while the MoE architecture is like a "division of labor" team, which only enables some "expert" sub-networks according to specific tasks. This not only saves computing resources, but also enables the model to exert more specialized capabilities on different tasks, thereby achieving efficient reasoning. The advantage of the MOE architecture is that it can maintain the overall capabilities of the model while achieving higher efficiency at a lower computing cost. The MOE architecture can be seen as a "team combat" mode:

• Expert team : The model integrates multiple "expert" sub-models, each of which focuses on a specific type of data or task.

• Dynamic activation : When receiving an input, the system will dynamically select some experts to participate in the calculation based on the characteristics of the input, rather than letting the entire large network participate. This greatly reduces unnecessary calculations.

• Improved efficiency : This approach not only speeds up the response of the model, but also enables the model to call the most appropriate "experts" when processing complex tasks, thereby achieving efficient and accurate reasoning.




• Data optimization : DeepSeek R1 carefully screens and preprocesses massive amounts of data before training. Through cleaning, denoising, and data augmentation, the model can focus more on high-quality information and reduce redundant calculations, thereby reducing training costs and improving overall performance.

  
  

• More efficient reasoning mechanism : Compared with GPT-4, DeepSeek R1 uses a lighter computing path in the reasoning stage to reduce redundant calculations.

? 2. Innovation in training methods: reinforcement learning + advanced distillation

DeepSeek R1 combines reinforcement learning (RLHF) and knowledge distillation to enable small models to have stronger reasoning capabilities:

• Reinforcement learning, especially human feedback reinforcement learning (RLHF) , plays a key role in the training of DeepSeek R1: during the training process, the model optimizes the output by continuously receiving human feedback. In simple terms, the model first generates answers, and then adjusts them based on human evaluations, so that the output is more in line with human expectations and logic. This mechanism greatly improves the performance of the model in real scenarios.

  
  

• Feedback mechanism : After generating answers, the model will receive human evaluation as feedback, indicating which answers are more reasonable and which are not accurate enough.

• Rewards and punishments : Based on feedback, the model will adjust its decision-making strategy and continuously optimize the generated results to make them more in line with human expectations.

• Continuous improvement : This training method allows the model to "learn" how to better solve the problem in continuous iterations, reducing errors and unreasonable answers (i.e. reducing hallucinations).

• Distillation : Knowledge distillation is a technique where a large, powerful "teacher model" guides a smaller "student model" to learn. DeepSeek R1 uses this approach to enable even smaller, computationally lighter models to inherit the high-quality reasoning capabilities of large models. This approach not only reduces the cost of running the model, but also makes it faster and more energy-efficient in practical applications.

• Teacher and Student Models : A large, high-performance “teacher model” is fully trained first, and then the “soft labels” or intermediate representations it produces are used to train a smaller, faster “student model.”

• Knowledge transfer : By imitating the output of the teacher model, the student model not only learns how to answer questions, but also captures the deep patterns contained in the teacher model.

• Reduce costs : This approach allows smaller models to approximate the effects of larger models in practical applications, while significantly reducing the computing resource requirements during inference.

? 3. Multimodal Capabilities & RAG (Retrieval Augmentation Generation) Optimization

RAG (Retrieval-Augmented Generation) is a key trend in the current AI field.

The core of RAG technology is to allow the model to not only rely on built-in knowledge to answer, but also retrieve external information in real time to supplement the answer. For example, when the model encounters an unfamiliar question, it will first find relevant content from a pre-built knowledge base or document collection, and then generate it in combination with the question, thereby reducing the risk of hallucination ( i.e. , the model generates inaccurate information).

DeepSeek R1 also has breakthroughs in this regard:

• More efficient retrieval strategies , reducing the problem of hallucinations.

• Intelligent Agents combined with RAG can automatically find the most relevant contextual information and provide it to the model for decision making. It can be understood as an "assistant" that helps the model obtain more comprehensive background knowledge when answering questions, making the generated content more accurate and credible.

? 4. Transparent reasoning process and support for fine-tuning

Unlike some closed business models, DeepSeek R1's internal operations and reasoning process are open source and transparent.

DeepSeek publicly displays every step of reasoning , while OpenAI's GPT-4 Turbo (o1), although it has powerful reasoning capabilities, strictly keeps its internal mechanisms confidential . This makes DeepSeek a powerful knowledge distillation tool , which not only allows developers to clearly understand how the model makes decisions, but also makes it easier for everyone to improve and innovate on this basis. Transparency allows more people to participate in model optimization, thereby continuously improving the technical level.

DeepSeek R1 also supports fine-tuning based on specific fields or tasks . Enterprises or developers can use their own data to retrain the model based on existing data, so that the model can better meet their actual needs.

? In short

The reason why DeepSeek R1 can challenge traditional large models at 1/30 of the cost is due to the synergy of multiple internal technologies:

• Reduce the computational burden through efficient data processing and MoE architecture,

• Using knowledge distillation, small models can also have the wisdom of large models.

• Coupled with reinforcement learning and RAG technology to enhance generation capabilities,

• All while maintaining open source transparency.

The combination of these technologies not only makes DeepSeek R1 low-cost and powerful, but also provides a flexible and easy-to-customize AI tool for developers and enterprises.

Through these innovations, DeepSeek R1 brings more possibilities to the entire AI ecosystem and provides new ideas for subsequent technological development. I hope the above introduction can help you better understand this powerful open source model.