DeepSeek-R1 is an open-weights large language model focused on advanced reasoning. It uses chain-of-thought processing and internal monologues to solve complex mathematical and logical problems by breaking tasks into sequential, verifiable thought processes.
The model is developed using reinforcement learning to optimize reasoning patterns and verify logical steps. It employs a distillation process to transfer these high-performance logic capabilities from a large teacher model into smaller, computationally efficient versions.
The training framework incorporates group relative policy optimization, cold-start supervised fine-tuning, and multi-stage model distillation. These methods are supported by large-scale compute orchestration across GPU clusters.
