DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Uses large-scale reinforcement learning with automatically checkable rewards, such as correct maths answers or passing code tests, to teach a model long step-by-step reasoning without human-written reasoning examples. Self-checking and backtracking emerged during training.

Why it matters

With openly released weights, it showed strong reasoning models can be reproduced openly at relatively low cost.

Read the paper: arXiv:2501.12948

Plain-language summary written by SolxLab. Read the full paper at the official link; all rights remain with the authors.