My Econometrics professor wasn’t the easiest lecturer to understand.
His preferred method of instruction was to explain complex concepts verbally and then translate those explanations into mathematical terms—often leaving many of my classmates and me confused after the lecture.
Sure, we may have taken down everything he wrote, but without proper context or formatting, it was incredibly difficult to recall what he said, let alone fully understand what he taught.
As I was reading the paper, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, DeepSeek-R1-Zero reminded me of him.
A key limitation of DeepSeek-R1-Zero is that its content is often not suitable for reading. Responses may mix multiple languages or lack markdown formatting to highlight answers for users.
To address these limitations, the DeepSeek team, comprising researchers and engineers, implemented multi-stage training and cold-start data, leading to the development of DeepSeek-R1.
The result?
DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks.
This could be incredibly valuable for business users looking to leverage a reasoning model for tasks such as market analysis or customer segmentation. With improved reasoning capabilities, businesses can make more informed decisions and execute better-targeted marketing campaigns with greater speed and accuracy.
Side note: I have nothing but the utmost respect for my professor—his brilliance ultimately inspired me to pursue my Master’s degree under his supervision.