DeepSeek's approach to training emphasizes compounding small improvements over time in data, architecture, and integration, which can lead to significant advancements.

The reasoning model of DeepSeek allows for deeper answers by thinking through the steps of a problem, which was not released with previous models like OpenAI's.

DeepSeek's R1 reasoning model achieves impressive performance by applying reinforcement learning specifically to think step by step through complex problems, similar to OpenAI's approach with their model, o1.

DeepSeek's training efficiency stems from two main techniques: a mixture of experts model and a new method called MLA latent attention, which reduces both training and inference costs.

DeepSeek's approach to AI training reflects a broader trend in the industry where companies are increasingly focused on optimizing both training and inference processes to enhance model performance.

Through thousands of reinforcement learning steps, DeepSeek's model learned advanced reasoning skills, including extended chains of thought and the ability to recognize and correct its own mistakes.

DeepSeek's recent models, including V3, highlight the importance of balancing training and inference compute resources to optimize performance and efficiency.

DeepSeek introduced novel techniques to efficiently train models with a mixture of experts architecture, stabilizing performance and increasing GPU utilization.

DeepSeek's R1 model is one of the first large models to achieve top-tier results purely through reinforcement learning, marking a significant milestone in AI development. Additionally, DeepSeek introduced a cold start phase for fine-tuning on structured reasoning examples before reinforcement learning, which eliminated language mixing issues and made outputs far more comprehensible.