DeepSeek R1 & Kimi 1.5: Inside the Development of Near‑Strong Reasoning Models
The article analyzes DeepSeek's recent releases—V3 dialogue model and R1 inference model—detailing their launch dates, rapid popularity surge, R1's reinforcement‑learning‑based design for code and math tasks, and provides links to related Beijing University technical reports while stripping promotional sales content.
DeepSeek, an open‑source AI lab, has released more than ten models, with the most discussed being the V3 dialogue model and the R1 inference model.
Both models were launched in quick succession: V3 on 2024‑12‑26 and R1 on 2025‑01‑20. Their releases caused a sharp rise in DeepSeek’s WeChat index, reaching about 6,000 万 on 2024‑12‑28 and 9.8 亿 on 2025‑01‑31.
R1 is an inference‑oriented model trained with reinforcement learning, targeting code generation and solving complex mathematical problems. Its reasoning ability can be transferred to smaller models via distillation techniques.
The article links to several earlier Beijing University technical reports on DeepSeek, including analyses of AIGC applications and the underlying principles, and provides references for readers to explore the full set of DeepSeek documentation.
While the original post contains many promotional links for paid handbooks and ebook bundles, the core technical insight focuses on the model architectures, training methods, release timeline, and market impact of DeepSeek’s recent models.
Signed-in readers can open the original source through BestHub's protected redirect.
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