Some Thoughts on Reasoning Models
At the beginning of the year, deepseek launched the r1 model, and OpenAI subsequently released the o3-mini model. After briefly reviewing the deepseek-r1 paper, I have some thoughts and questions about reasoning models.
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First, a disclaimer: I don’t have a deep understanding of the principles behind LLMs, so if there are any misunderstandings, please feel free to correct me.
From the deepseek-r1 paper, it can be seen that the model is based on the deepseek-v3 as the foundation model, which is then fine-tuned through RL to obtain a reasoning model. Other distilled models are also built on top of other open-source models, using the output of r1 as training data.
This raises some questions for me:
1.Are these so-called reasoning models truly ‘reasoning’? 2.Is it possible to enable reasoning capabilities in the base model through Prompt Engineering? For example, by using CoT + few-shot methods?