DeepSeek R1 Unpacked: Reinventing AI Reasoning, IP Challenges & Breaking Censorship Barriers

The State of Industrial AI - E86

DeepSeek R1 Changes Everything….Or does it?

DeepSeek R1 has risen rapidly to challenge or approximate the performance of leading US AI models, including OpenAI’s O1 series. According to reported benchmark scores, DeepSeek R1 appears only marginally behind top-tier models in reasoning tasks, yet operates at a fraction of the cost….Is what every news outlet is reporting. Let’s take a look under the hood…

Ups…Oh no! What in the world are we looking at? Seems like DeepSeek will have to do some explaining as it clearly states that it was distilled from OpenAI’s ChatGPT.

Technical Innovations

Despite the illegal IP theft from OpenAI, we are still looking at some potentially revolutionary innovations. DeepSeek R1’s approach to reasoning is unique due to its reliance on pure reinforcement learning (RL), which allows it to self-improve without traditional supervised fine-tuning. Unlike OpenAI’s models, which combine reinforcement learning with human feedback (RLHF), DeepSeek’s model optimizes for reasoning accuracy purely through RL objectives.

Key Technical Features

• Chain-of-Thought Reinforcement Learning: Instead of being explicitly trained on human-annotated logical reasoning, DeepSeek R1 learns to improve its own thought process over time by iterating on its previous mistakes.

  

• Group Relative Policy Optimization (GRPO): This technique optimizes reasoning by comparing batches of responses rather than requiring human-rated reward models. This makes training scalable and cost-effective.

• Distillation of Reasoning Capabilities: Unlike many open-weight models that struggle with complex problem-solving, DeepSeek transfers its reasoning skills to smaller models, making them highly efficient.

Benchmark Distortions

Misalignment Between Benchmark Design and DeepSeek R1’s Training

Most reasoning-based benchmarks assume that models will improve based on exposure to large-scale labeled data. DeepSeek R1, however, acquires its reasoning capabilities primarily through trial-and-error exploration in reinforcement learning. This means that its strengths lie in areas where self-improvement and iterative problem-solving are effective, but it might underperform in benchmarks optimized for pattern recognition or retrieval-based knowledge.

For example:

Mathematical and Coding Tasks (MATH-500, LiveCodeBench, Codeforces)

DeepSeek R1 achieves 97.3% on MATH-500, outperforming even OpenAI-o1-1217. However, its results are distorted by the fact that the benchmark was tuned for models with process-based reward modeling rather than outcome-driven reinforcement learning. DeepSeek R1 may be solving problems in unconventional ways that aren’t always captured by traditional grading rubrics.

General Knowledge and Factual Recall (MMLU, GPQA Diamond, SimpleQA)

Despite strong reasoning capabilities, DeepSeek R1 underperforms on some factual benchmarks like SimpleQA, where retrieval-based architectures have an advantage. Since the model was not trained explicitly on vast supervised datasets, it may struggle with rapid factual lookup tasks that GPT-4o or Claude handle more efficiently.

Safety and Alignment Evaluations (Arena-Hard, AlpacaEval 2.0)

Because DeepSeek R1 optimizes for reinforcement learning rewards rather than human-supervised preference modeling, its responses on subjective benchmarks like AlpacaEval 2.0 (87.6% win-rate) or Arena-Hard (92.3% win-rate) may not always align with expectations of “helpfulness” in AI assistant benchmarks.

Geopolitical and Internal Implications of DeepSeek like LLMs in China

The release of an advanced reasoning AI like DeepSeek-R1 in an open-source format has significant geopolitical ramifications, especially considering its ability to run locally on consumer hardware. This shift brings major challenges for information control in China, where the government tightly regulates AI and large-scale internet access. Unlike centralized cloud-hosted AI services, locally runnable models like DeepSeek-R1 cannot be effectively fire walled or censored, giving individuals an unprecedented ability to access information that was previously restricted.

Information Control in China: A Crumbling Barrier

Historically, China’s “Great Firewall” has been highly effective at blocking external sources of politically sensitive content. However, open-weight AI models challenge this paradigm. When an AI model is freely available and can be downloaded onto personal devices, state censorship mechanisms become far less effective. Even if the Chinese government were to ban the official distribution of DeepSeek-R1, it would be virtually impossible to prevent people from sharing and side loading the model onto their local machines.

We tested DeepSeek R1 32B Parameter Uncensored Model for you!

What does the model know about what happened in 1989 on Tianamen Square?

“DeepSeek-R1 refuses to answer a question about Tiananmen Square, explicitly stating that it was created by a Chinese company.”

The fact that DeepSeek-R1 initially refuses to answer a politically sensitive question suggests that, at least on the surface, the model aligns with Chinese government expectations. However, this default refusal is not the same as active censorship—it is merely a built-in safety filter. The real test of information accessibility comes when users experiment with alternative phrasing, as seen in the following case.

“When framed differently, DeepSeek-R1 provides an extensive, largely uncensored explanation of the Tiananmen Square protests and government response.”

As seen above, when asked in a different way, the model provides a historically accurate response. This suggests that DeepSeek-R1’s training data is not entirely sanitized and that its ability to access historical knowledge is still intact. This is a significant development, as it implies that users in China could use local AI models to retrieve information previously unavailable to them through government-controlled sources.

While Chinese AI firms typically align with national policies, the fact that DeepSeek-R1 has not been aggressively censored may indicate a more complex interplay between research freedom and state oversight.

How to Install and Run the Uncensored DeepSeek R1 Locally