DeepSeek-R1 is a language model focused on enhanced reasoning, employing reinforcement learning (RL) and building upon the DeepSeek-V3-Base model. It uses Group Relative Policy Optimization (GRPO) to reduce computational costs by eliminating the need for a separate critic model, which is commonly used in other algorithms such as PPO. The model uses a multi-stage training pipeline including an initial fine-tuning with cold-start data, followed by reasoning-oriented RL, and supervised fine-tuning (SFT) using rejection sampling, and a final RL stage. A rule-based reward system avoids reward hacking. DeepSeek-R1 also employs a language consistency reward during RL to address language mixing. The model's reasoning capabilities are then distilled into smaller models. DeepSeek-R1 achieves performance comparable to, and sometimes surpassing, OpenAI's o1 series on various reasoning, math, and coding tasks.