Introducing DeepSeek-R1: Advancing AI Reasoning with Reinforcement Learning
A New Era in AI Reasoning
The field of artificial intelligence is evolving rapidly, and reasoning capabilities are at the forefront of this transformation. DeepSeek AI introduces its latest innovation—DeepSeek-R1, a first-generation reasoning model built through large-scale reinforcement learning (RL). Alongside DeepSeek-R1, we also present DeepSeek-R1-Zero, an RL-trained model developed without supervised fine-tuning (SFT). Both models showcase impressive performance in reasoning tasks, marking a significant milestone in AI research.
What Makes DeepSeek-R1 Special?
DeepSeek-R1-Zero was trained purely with RL, bypassing the traditional SFT step. This approach allowed the model to naturally develop advanced reasoning behaviors, including self-verification, reflection, and structured problem-solving. However, challenges such as repetition and language inconsistencies emerged. To address these, we introduced DeepSeek-R1, incorporating a cold-start dataset before RL training to improve performance and coherence.
With these enhancements, DeepSeek-R1 achieves comparable performance to OpenAI-o1 across tasks involving math, coding, and logical reasoning. The model sets a new benchmark in AI capabilities, proving that reasoning skills can be developed through reinforcement learning alone.
Post-Training and Distillation: Powering Smarter AI
Reinforcement Learning for Advanced Reasoning
DeepSeek-R1’s training pipeline consists of two RL stages, refining its ability to reason and align with human preferences. Additionally, two SFT stages were used to seed the model’s reasoning and general capabilities, further strengthening its output quality.
Smaller Models, Big Impact
One of the most exciting advancements is the distillation of DeepSeek-R1 into smaller, high-performing models. By transferring knowledge from larger models to smaller architectures, we enhance efficiency without sacrificing capability. Our open-source distilled models, ranging from 1.5B to 70B parameters, demonstrate state-of-the-art results across multiple benchmarks. Notably, the DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini on key evaluation metrics.
Benchmark Performance
DeepSeek-R1 and its distilled models excel in multiple domains, achieving remarkable results on:
Math: Achieving top scores on AIME 2024 and MATH-500.
Coding: Setting new records on Codeforces and LiveCodeBench.
Reasoning: Outperforming competitors on MMLU and GPQA benchmarks.
Multilingual Understanding: Excelling in English and Chinese language tasks.
DeepSeek-R1 Evaluation Results
General Model Comparison
| Category | Benchmark (Metric) | Claude-3.5-Sonnet-1022 | GPT-4o 0513 | DeepSeek V3 | OpenAI o1-mini | OpenAI o1-1217 | DeepSeek R1 |
|---|---|---|---|---|---|---|---|
| English | MMLU (Pass@1) | 88.3 | 87.2 | 88.5 | 85.2 | 91.8 | 90.8 |
| MMLU-Redux (EM) | 88.9 | 88.0 | 89.1 | 86.7 | - | 92.9 | |
| MMLU-Pro (EM) | 78.0 | 72.6 | 75.9 | 80.3 | - | 84.0 | |
| Code | LiveCodeBench (Pass@1-COT) | 33.8 | 34.2 | - | 53.8 | 63.4 | 65.9 |
| Codeforces (Percentile) | 20.3 | 23.6 | 58.7 | 93.4 | 96.6 | 96.3 | |
| Math | AIME 2024 (Pass@1) | 16.0 | 9.3 | 39.2 | 63.6 | 79.2 | 79.8 |
| MATH-500 (Pass@1) | 78.3 | 74.6 | 90.2 | 90.0 | 96.4 | 97.3 | |
| Chinese | CLUEWSC (EM) | 85.4 | 87.9 | 90.9 | 89.9 | - | 92.8 |
| C-Eval (EM) | 76.7 | 76.0 | 86.5 | 68.9 | - | 91.8 |
Distilled Model Evaluation
| Model | AIME 2024 Pass@1 | AIME 2024 Cons@64 | MATH-500 Pass@1 | GPQA Diamond Pass@1 | LiveCodeBench Pass@1 | CodeForces Rating |
|---|---|---|---|---|---|---|
| GPT-4o-0513 | 9.3 | 13.4 | 74.6 | 49.9 | 32.9 | 759 |
| Claude-3.5-Sonnet-1022 | 16.0 | 26.7 | 78.3 | 65.0 | 38.9 | 717 |
| o1-mini | 63.6 | 80.0 | 90.0 | 60.0 | 53.8 | 1820 |
| QwQ-32B-Preview | 44.0 | 60.0 | 90.6 | 54.5 | 41.9 | 1316 |
| DeepSeek-R1-Distill-Qwen-1.5B | 28.9 | 52.7 | 83.9 | 33.8 | 16.9 | 954 |
| DeepSeek-R1-Distill-Qwen-7B | 55.5 | 83.3 | 92.8 | 49.1 | 37.6 | 1189 |
| DeepSeek-R1-Distill-Qwen-14B | 69.7 | 80.0 | 93.9 | 59.1 | 53.1 | 1481 |
| DeepSeek-R1-Distill-Qwen-32B | 72.6 | 83.3 | 94.3 | 62.1 | 57.2 | 1691 |
| DeepSeek-R1-Distill-Llama-8B | 50.4 | 80.0 | 89.1 | 49.0 | 39.6 | 1205 |
| DeepSeek-R1-Distill-Llama-70B | 70.0 | 86.7 | 94.5 | 65.2 | 57.5 | 1633 |
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How to Access and Use DeepSeek-R1
DeepSeek-R1 is available for research and development through multiple platforms:
Chat Interface: Try it live on DeepSeek Chat
API Access: OpenAI-compatible API on DeepSeek Platform
Model Downloads: Available on Hugging Face
Run Locally: Use frameworks like vLLM and SGLang for local deployment.
Best Practices for Using DeepSeek-R1
To maximize performance when using DeepSeek-R1, follow these recommendations:
Set the temperature between 0.5 and 0.7 to optimize output coherence.
Avoid system prompts—all instructions should be in the user prompt.
For math problems, instruct the model to reason step-by-step and format answers clearly.
Conduct multiple tests and average results for accurate benchmarking.
Open-Source Commitment and Licensing
DeepSeek-R1 and its distilled models are open-source under the MIT License, allowing commercial use and modification. The distillation models are based on Qwen and Llama architectures, incorporating advanced reasoning capabilities developed with DeepSeek AI’s training pipeline.
Join the Future of AI Reasoning
The launch of DeepSeek-R1 marks a significant step forward in AI reasoning research. By leveraging reinforcement learning and innovative training techniques, DeepSeek AI is shaping the future of intelligent systems.
Explore DeepSeek-R1 today and be part of this groundbreaking journey!
For more details, visit DeepSeek AI or check out the DeepSeek-R1 repository.
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