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Book Overview & Buying
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Table Of Contents
DeepSeek in Practice
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In this chapter, we delved into the core research and technical evolution behind DeepSeek, tracing its trajectory. We examined how DeepSeek’s development splits into two powerful threads: the construction of foundational models and the advancement of reasoning capabilities. Each successive version of DeepSeek added a new layer of innovation, from scaling dense architectures to pioneering efficient MoE models, and, ultimately, refining multi-step reasoning through rule-based reinforcement learning.
We explored how the adoption of MoE techniques led to significant performance gains at lower compute costs, and how innovations such as MLA, MTP, and GRPO addressed key challenges in training stability, inference efficiency, and reasoning reliability. The chapter also highlighted DeepSeek’s specialized efforts in areas such as code intelligence, mathematics, and formal logic, illustrating its versatile application across domains.
The release of DeepSeek-R1 represents...