Introdução ao novo framework de treino da DeepSeek

DeepSeek Introduces New AI Training Framework: Manifold-Constrained Hyper-Connections (mHC)

In a groundbreaking announcement, DeepSeek, a pioneer in artificial intelligence innovation, has unveiled its latest AI training framework, the Manifold-Constrained Hyper-Connections (mHC). This framework addresses critical challenges in scalability and stability in AI model training, presenting a significant evolution in how large models are developed.

Current Challenges in AI Model Training

AI models today commonly encounter several issues:

Instability During Training: As models grow in size, the addition of unconstrained residual connections can lead to signal amplification, resulting in instability—especially in deep networks exceeding 60 layers.
Scalability Challenges: When the spectral patterns of connection matrices surpass a value of 1, effective training beyond certain model sizes becomes impractical.

What is the Manifold-Constrained Hyper-Connections (mHC)?

The mHC framework was described in the technical paper arXiv:2512.24880 published on December 31, 2025. Here’s a simplified breakdown of its core concepts:

Design of Residual Connection Matrices: Unlike standard residual connections that add input directly to the output of a layer with a fixed weight, mHC employs learned mixing matrices to control multiple parallel flows, enhancing flexibility.
Utilization of the Birkhoff Polytope: The mHC framework projects these matrices into the realm of doubly stochastic matrices, ensuring that the signal magnitude remains stable, thereby promoting a more reliable training process.

Advantages of mHC

The mHC framework offers numerous advantages for the field of AI, including:

Enhanced Stability: The projection to the Birkhoff polytope significantly reduces unwanted amplifications in deep networks.
Capacity to Scale Models to Trillions of Parameters: The framework facilitates innovations such as layer-specific connection forces, enabling the construction of even more complex models.

Implications for the Industry

The introduction of mHC has profound implications for various sectors that rely on AI:

Healthcare: Improved AI models can lead to more accurate diagnostics and personalized treatment plans.
Finance: Enhanced model stability can improve fraud detection systems and risk assessment strategies.
Transportation: Advanced AI frameworks can optimize routing algorithms, providing smarter logistics solutions.

Testing Outcomes and Innovations

DeepSeek has conducted tests on models based on the DeepSeek-V3 architecture, with parameters ranging from 3 billion to 27 billion. The results demonstrated that mHC:

Exceeds Baselines Compared to Unconstrained HC: Significant performance and scalability improvements were observed.
Reduces Memory Access Overhead: Optimizations not only enhanced performance but also made resource usage more efficient.

Model	Parameters	Performance
DeepSeek-V3	3B	High
DeepSeek-V3	9B	Superior
DeepSeek-V3	27B	Excellent

Conclusion

The introduction of the Manifold-Constrained Hyper-Connections framework by DeepSeek not only addresses critical issues related to scalability and stability but also redefines the potential for training AI models. With the advancements enabled by this technique, there is a significant opportunity to explore and implement robust, complex models, leading to transformative impacts in the research and application of artificial intelligence in the coming years.

For more in-depth information about this innovative framework, you can consult the full article on arXiv or explore additional discussions on DeepSeek’s official blog.

Sources

By enhancing the potential applications, making the language accessible, and optimizing keyword usage, this article now clearly communicates the significance of DeepSeek’s innovations in AI training frameworks.

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