DeepSeek has released a series of significant papers detailing advancements in large language models (LLMs). Each paper represents a step forward in making AI more capable, efficient, and accessible.
This foundational paper explores scaling laws and the trade-offs between data and model size, establishing the groundwork for subsequent models.
Introduces a Mixture-of-Experts (MoE) architecture, enhancing performance while reducing training costs by 42%.
Discusses the scaling of sparse MoE networks to 671 billion parameters.
Enhances reasoning capabilities through large-scale reinforcement learning.
Presents methods to improve mathematical reasoning in LLMs.
Focuses on enhancing theorem proving capabilities using synthetic data for training.
Details advancements in code-related tasks with emphasis on open-source methodologies.
Discusses the integration and benefits of the Mixture-of-Experts approach.