DeepSeek-R1: Incentivizing Reasoning Capability in LLM via RL (Guo et al., 2025)

Welcome to Revise and Resubmit, where we take a deep dive into the latest breakthroughs in research, unraveling the complexities of cutting-edge ideas—one paper at a time.

Today, we embark on a journey into the mind of AI itself. Imagine a language model not just trained to predict words but to reason, to think, to solve—not through conventional programming, but through the power of reinforcement learning. The DeepSeek-AI team introduces DeepSeek-R1, a model that learns by trial and error, sharpening its reasoning skills like a grandmaster refining their game. But can machines truly learn reasoning the way we do? And if so, what does this mean for the future of AI-driven intelligence?

A huge thanks to the DeepSeek-AI team for this fascinating research. Don’t forget to subscribe to Revise and Resubmit on Spotify and check out Weekend Researcher on YouTube. You can also find us on Amazon Prime and Apple Podcasts. Until next time—what happens when machines start reasoning better than humans?

Reference

Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., ... & He, Y. (2025). DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arXiv preprint arXiv:2501.12948. https://doi.org/10.48550/arXiv.2501.12948

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