What’s the Big Deal About DeepSeek-R1?

Have you ever used an AI and thought, “Wow, it’s smart, but it doesn’t quite get me”?
Many AI models excel at basic tasks but stumble when things get tricky, like solving math problems or understanding nuanced questions. That’s where DeepSeek R1 shines.

DeepSeek-R1, developed by the team at DeepSeek-AI, is a cutting-edge reasoning model designed to tackle these challenges head-on. Its secret sauce? A training process powered by reinforcement learning (RL). Unlike traditional models that rely on massive amounts of labeled data to learn, DeepSeek R1 uses trial and error, just like humans do, to refine its reasoning skills.

However, recent developments have raised concerns about DeepSeek’s data practices. Reports indicate that Microsoft and OpenAI are investigating whether individuals linked to DeepSeek improperly accessed OpenAI’s systems, potentially transferring significant amounts of data through OpenAI’s API. Allegations suggest that DeepSeek may have employed “distillation” techniques to train its models using OpenAI’s outputs without explicit permission. These claims have sparked industry-wide discussions and even a national security assessment by the U.S. government. As of now, DeepSeek has yet to respond publicly to these allegations, but the AI community is closely watching the unfolding situation.

OpenAI has found evidence suggesting that DeepSeek utilized a technique called “distillation” to train its models using data from OpenAI’s outputs. This method involves creating smaller models by learning from the responses of larger ones, which, if done without permission, could violate OpenAI’s terms of service.
The Chinese neural network is available for free. The release of R1 caused a drop in Nvidia’s stock prices, as DeepSeek engineers managed to train a large-scale neural network without relying on expensive GPUs that are subject to export control sanctions for developers in China.

On January 27, 2025, the DeepSeek app claimed the top spot in the ranking of free apps in the U.S. App Store. On the same day, the company temporarily suspended new user registrations due to a DDoS attack on its web application and API service.

Benchmark performance of DeepSeek-R1

What Makes DeepSeek-R1 Special?

DeepSeek-R1-Zero
Picture a model that starts with zero guidance. DeepSeek-R1-Zero was trained purely through RL, meaning it figured out how to reason by exploring different approaches and learning from mistakes. It showed incredible potential, scoring 71% on the AIME 2024 benchmark—a significant leap from its initial 15%.
However, it wasn’t perfect. Its responses sometimes mixed languages or lacked clarity.
DeepSeek-R1
To tackle these issues, the team introduced DeepSeek-R1. They started by giving it a “cold start”—a small set of high-quality examples to fine-tune its understanding. By incorporating cold-start data and a multi-stage training pipeline, DeepSeek-R1 improved upon DeepSeek-R1-Zero and achieved performance comparable to OpenAI-o1-1217 on reasoning tasks. This helped the model produce clear, coherent answers while maintaining its reasoning prowess. The result? A model that’s as readable as it is smart, scoring on par with OpenAI’s best models in several benchmarks.

Comparison of DeepSeek-R1-Zero and OpenAI o1 models on reasoning-related benchmarks.

Imagine teaching a dog a new trick. Every time it performs the trick correctly, you give it a treat. Over time, the dog learns to associate the trick with the reward.

DeepSeek-R1-Zero learned similarly. Instead of treats, it received rewards for accurate, logical reasoning. The model explored various problem-solving methods, refined its techniques, and—most importantly—developed the ability to think through complex tasks.

DeepSeek-R1 learned to:

Solve advanced math problems.
Write code that works (on the first try).
Understand complex logic puzzles.

Comparison of DeepSeek-R1 distilled models and other comparable models on reasoning-related benchmarks

The team distilled DeepSeek-R1’s reasoning capabilities into smaller, more efficient models like Qwen and Llama. Despite being lighter, these models packed a punch, outperforming many open-source models on benchmarks like MATH-500 and GPQA Diamond. This means developers and researchers can harness DeepSeek-R1’s power without needing enormous computational resources.

For instance, the distilled 14B model achieved a whopping 94.3% accuracy on MATH-500—a score that’s almost unheard of for models its size. It’s like carrying the brain of a supercomputer in a smartphone.

Why This Matters

If you are a developer, researcher, or just someone who is interested in AI, then DeepSeek-R1 is a significant improvement. It’s not just a matter of finding answers, it’s a matter of how the AI comes up with them.

DeepSeek-R1’s reasoning capabilities mean:

Whether it’s coding, math, or logical reasoning, this model excels at tackling challenges.
The distillation process makes these advanced capabilities available in smaller, more affordable models.
Models like DeepSeek-R1 bring us closer to creating AI systems that think and learn like humans.

The Future of DeepSeek R1

The team at DeepSeek-AI isn’t stopping here. They’re already exploring ways to:

Expand DeepSeek-R1’s capabilities in areas like role-playing and multi-turn dialogues.
Improve its performance in software engineering tasks.
Address language-mixing issues for better multilingual support.

DeepSeek-R1 is a testament to what’s possible when we push the boundaries of AI. By combining reinforcement learning, distillation, and human ingenuity, it’s setting new standards for reasoning in language models.

Yet, the surrounding controversy underscores the complexities of AI ethics and competition. While DeepSeek-R1 demonstrates remarkable technological advancements, the unresolved questions about its data practices remind us that AI’s future must be built on transparency and trust.