DeepSeek-R1, an open-source reasoning model, has made waves in the AI community by achieving performance comparable to top foundation models while using a remarkably low training budget. This achievement challenges conventional wisdom about the scaling laws of foundation models and highlights the potential of novel post-training techniques. The model's open-weights nature has spurred rapid adoption and cloning, showcasing its significant impact. DeepSeek-R1 also underscores the rising prominence of Chinese AI models in driving innovation.
The artificial intelligence world was taken by storm a few days ago with the release of DeepSeek-R1, an open-source reasoning model that matches the performance of top foundation models while claiming to have been built using a remarkably low training budget and novel post-training techniques.
Most major foundation models – including those developed by OpenAI, Google, and Anthropic – adhere to this same general process. At a high level, DeepSeek-R1’s training procedure does not appear significantly different. ButHowever, rather than pretraining a base model from scratch, R1 leveraged the base model of its predecessor, DeepSeek-v3-base, which boasts an impressive 617 billion parameters.
R1-Zero is quite impressive, as it was able to match GPT-o1 in reasoning tasks. However, the model struggled with more general tasks such as question-answering and readability. That said, the purpose of R1-Zero was never to create a generalist model but rather to demonstrate it is possible to achieve state-of-the-art reasoning capabilities using reinforcement learning alone – even if the model does not perform well in other areas.
R1-Zero demonstrated that it is possible to develop sophisticated reasoning capabilities using basic reinforcement learning. Although R1-Zero was not a strong generalist model, it successfully generated the reasoning data necessary for R1.
Artificial Intelligence AI Deeplearning Foundationmodels Opensource Reasoning
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