Archive · 2 Jan 2026
DeepSeek Advances AI Training Efficiency in China
DeepSeek, a Chinese company, has come up with a new way to train AI models that uses less energy and is more scalable.
This is important because China doesn't have access to the best computer chips from companies like Nvidia, so they have to find creative solutions.
DeepSeek's founder, Liang Wenfeng, has been leading this research.
They tested this new method on different sizes of AI models and found it works well.
They also mentioned that this could help make better AI models in the future.
DeepSeek is known for making big announcements after publishing research papers, so people are excited to see what their next big AI model, called R2, will be like.
DeepSeek published a new AI training framework called Manifold-Constrained Hyper-Connections to improve scalability and reduce computational demands.
The framework was tested on models ranging from 3 billion to 27 billion parameters, addressing training instability and limited scalability.
DeepSeek is expected to release its next flagship model, R2, around the Spring Festival in February.
Chinese AI startups face significant constraints due to US restrictions on advanced semiconductors, forcing them to innovate with unconventional methods.
DeepSeek's previous model, R1, was developed at a fraction of the cost of its Silicon Valley rivals and stunned the industry.
- Who
- DeepSeek, a Chinese AI startup founded by Liang Wenfeng
- What
- Published a new AI training framework called Manifold-Constrained Hyper-Connections
- When
- Recently, with the next flagship model R2 expected around February
- Where
- China, specifically Hangzhou
- Why
- To improve scalability and reduce computational and energy demands of training advanced AI systems
- Company
- DeepSeek
- Founder
- Liang Wenfeng
- New Framework
- Manifold-Constrained Hyper-Connections
- Expected Model Release
- R2 (around Spring Festival, February)
- Model Parameters Tested
- 3 billion to 27 billion
- Research Platforms
- arXiv, Hugging Face
- Number of Authors
- 19
- Key Challenge Addressed
- Training instability and limited scalability