DeepSeek Shakes up the LLMs/Reinforcement and Recursive Self Learning/Synthetic Data

China made ground-breaking news in early 2025 when the DeepSeek-V3 R1 AI large language was announced. The developers claim to have created the system with only $5.6 million dollars in investment (compared to the normal U.S. AI model training budgets of $50 to $100 million) and to have done so quickly, with a lower total server investment. The model is open source, and fees charged for accessing and using the model’s data are significantly lower than those of competing models. This announcement from DeepSeek, established in 2023 in Hangzhou and funded by the hedge fund High Flyer, totally shook up competitors worldwide. DeepSeek planned to launch an advanced R2 version quickly, and will likely grow through continual development of newer, enhanced models.

DeepSeek utilized several advanced strategies, including Reinforcement Learning and Distillation. Reinforcement Learning encourages the system to experiment and improve itself continually. Distillation is a process of creating a smaller, more efficient language model by “distilling” existing larger language models (which may include the generation of “synthetic” data, or new data generated during this process).

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