DeepSeek's method for obtaining good results involves a combination of innovative techniques and strategic approaches. Here's a simplified explanation:
- Reinforcement Learning (RL): DeepSeek uses RL to train its models, which means the models learn by trial and error, receiving rewards or penalties for their actions. This allows them to develop reasoning abilities without needing labeled data.
- Multi-Stage Training: To overcome initial limitations, DeepSeek uses a multi-stage training process. This involves:
- Cold Start Fine-Tuning: A small, high-quality dataset is created to kick-start reasoning ability and improve coherence.
- Distillation Process: Advanced reasoning capabilities are transferred to smaller, more efficient models through distillation, making powerful AI accessible and cost-effective.
- Innovative Model Architectures: DeepSeek's models are designed to mimic human reasoning, focusing on chain-of-thought (CoT) and logical reasoning. This is achieved through novel architectures that prioritize coherence and readability.
- Efficient Resource Utilization: DeepSeek optimizes resource usage, leveraging older semiconductor chips (like Nvidia A100 GPUs) to train models, which reduces costs and sidesteps U.S. export restrictions.
- Strategic Planning and Talent: DeepSeek's success is driven by efficient resource use, strategic planning, and a robust AI talent pool, enabling rapid innovation and adaptation.
By combining these methods, DeepSeek achieves remarkable results in reasoning and AI development, offering alternatives to traditional AI systems at a fraction of the cost.