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

Benchmark Model Rank Results
question-answering-on-drop-3-shotDeepSeek-R1 (3-shot)#1F1: 92.2
question-answering-on-framesDeepSeek-R1#1Accuracy: 82.5
multi-task-language-understanding-on-c-evalDeepSeek-R1-Zero#2Accuracy: 92.8
question-answering-on-simpleqaDeepSeek-R1-Zero#3Accuracy: 30.3
code-generation-on-aider-polyglotDeepSeek-R1#4Accuracy: 53.3
question-answering-on-c-simpleqaDeepSeek-R1-Zero#4Accuracy: 66.4
question-answering-on-newsqadeepseek-r1#4EM: 80.57F1: 86.13
code-generation-on-humanevalDeepSeek R1#5Pass@1: 97.4
instruction-following-on-ifevalDeepSeek-R1#7Prompt-level strict-accuracy: 83.3
code-generation-on-livecodebench-v5DeepSeek-R1#8Pass@1: 65.9
multi-task-language-understanding-on-mmlu-reduxDeepSeek-R1#9Accuracy: 92.9
mathematical-reasoning-on-math-500DeepSeek-R1#13Accuracy: 97.3
multi-task-language-understanding-on-mmlu-proDeepSeek-R1#24Accuracy: 84.0
mathematical-reasoning-on-aime24DeepSeek-r1#31Acc: 79.8
multiple-choice-question-answering-mcqa-on-gpqa-diamondDeepSeek-R1-Zero#66Accuracy: 75.8
code-generation-on-swe-bench-verifiedDeepSeek-R1#76Accuracy: 49.2