Welcome to today's podcast, where we delve into the innovative DeepSeek series, exploring its groundbreaking technical reports. Starting with DeepSeek-LLM, released in January 2024, this paper tackles the scaling laws of language models, posing the critical question: "How do we balance model size and training data?" Their formula shows that computing power can be predicted based on model characteristics and data quality, highlighting the importance of high-quality data.

Moving to DeepSeek-V2 in June 2024, we see the introduction of Multi-Head Latent Attention and a sparse Mixture-of-Experts approach. This novel architecture reduces memory usage and increases efficiency, allowing for the training of models with238 billion parameters while keeping costs down.

In December 2024, DeepSeek-V3 pushes the boundaries further, scaling to 671 billion parameters. The report emphasizes co-designing algorithms and frameworks to optimize training efficiencies and communication overhead, achieving remarkable results with minimal resource expenditure.

Finally, DeepSeek-R1, released in January 2025, transforms our understanding of reasoning by utilizing large-scale reinforcement learning to cultivate sophisticated chain-of-thought capabilities. As the authors put it, “Pure RL yields strong reasoning in verifiable tasks,” marking a significant milestone in AI development.

Tune in next time as we dissect these findings and their implications for the future of language models!
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