DeepSeek's R1 models achieved impressive results on math and coding problems using reinforcement learning, a cheaper training method that rewards correct answers rather than relying on human-labeled data. However, researchers caution that the models' ability to explain their reasoning doesn't necessarily mean they think like humans, and the true mechanics of how these systems work remain largely mysterious .

What Makes DeepSeek's Training Method So Much Cheaper?

Traditional AI training for reasoning tasks requires thousands of human-annotated examples showing models exactly how to solve problems step by step. This approach demands enormous computing power and expensive human labor. DeepSeek took a different path, using reinforcement learning, which works more like teaching through trial and error .

Instead of being told the correct steps, DeepSeek's models make multiple attempts at solving a problem and receive a simple reward signal: 1 point for a correct answer, 0 for an incorrect one. The model learns to recognize patterns that lead to correct solutions without explicit instruction.

"Rather than supervise the LLM's every move, researchers instead only tell the LLM how well it did," explained Emma Jordan, a reinforcement learning researcher at the University of Pittsburgh.

Emma Jordan, Reinforcement Learning Researcher at University of Pittsburgh

This approach dramatically reduces the need for expensive human annotation and can lower overall training costs. The catch is that it only works well when the underlying model is already reasonably good at guessing correct answers .

How Does DeepSeek's Trial-and-Error Process Actually Work?

DeepSeek's R1-Zero model, trained purely through reinforcement learning, operates through a surprisingly simple mechanism. During training, the model generates multiple candidate solutions to a math or coding problem, typically around 15 different attempts. If any of those attempts is correct, the model receives a reward signal for those successful paths. The incorrect attempts receive no feedback .

This creates a potential problem: if all 15 guesses are wrong, the model learns nothing from that attempt. For this system to work, DeepSeek needed a foundation model that was already competent enough to include correct answers within its top guesses. Fortunately, DeepSeek's V3 Base model already performed better than older systems like OpenAI's GPT-4o on reasoning problems, giving it a head start .

DeepSeek then refined this approach by adding additional reward signals beyond just accuracy. The company introduced format rewards that encouraged the model to describe its reasoning process and label that description before providing the final answer. This led to the creation of DeepSeek-R1, which performed better than R1-Zero but also produced cleaner, more readable outputs .

Steps to Understanding DeepSeek's Training Innovation

• Reinforcement Learning Basics: Instead of showing models the correct path, researchers reward correct outcomes and let the model discover patterns that lead to success through repeated attempts.
• Reward Structure Design: DeepSeek uses two types of rewards: accuracy rewards that verify answers against correct solutions, and format rewards that encourage clear explanation of reasoning steps.
• Foundation Model Quality: The success of this approach depends on starting with a base model that's already reasonably skilled, so correct answers appear within its top candidate responses.
• Iterative Refinement: DeepSeek improved its initial R1-Zero model by adding additional training stages to fix problems like language mixing, creating the more polished R1 version.

Does This Mean AI Models Can Actually Reason Like Humans?

This is where the story gets complicated. DeepSeek's models produce outputs that look remarkably like human reasoning. They explain their thought process, note where they might need to double-check work, and describe their problem-solving approach in natural language. It's tempting to conclude that these systems are genuinely thinking .

But researchers are skeptical of this interpretation.

"We don't really understand how the models work internally and its outputs have been overly anthropomorphized to imply that it is thinking," noted Subbarao Kambhampati, a computer scientist at Arizona State University who peer-reviewed DeepSeek's Nature publication.

Subbarao Kambhampati, Computer Scientist at Arizona State University

The humanlike explanations may simply be a side effect of the format reward signal. By training the model to describe its reasoning before providing answers, DeepSeek created outputs that sound thoughtful and deliberate. But this doesn't necessarily reveal what's actually happening in the model's internal computations .

Kambhampati emphasized that understanding the true mechanics of AI reasoning remains an active research problem. The fact that a model produces the right answer through a process that looks like reasoning doesn't prove the model is reasoning in any meaningful sense .

Why Did DeepSeek Open Its Models to Scientific Scrutiny?

Perhaps most surprising, DeepSeek submitted its models for peer review and publication in Nature, a top-tier scientific journal. This is rare for AI companies, which typically guard their models closely. By allowing independent researchers to examine and test the models, DeepSeek provided an unprecedented opportunity to verify its claims .

This transparency excited the research community, not just because of the impressive performance numbers, but because it offered a chance to look inside the "black box" of AI systems.

"DeepSeek basically showed its hand, so that others can verify and improve the algorithms," said Kambhampati, adding that "that's how science is supposed to work."

Subbarao Kambhampati, Computer Scientist at Arizona State University

The peer review process revealed important details about how the models were trained, including a significant caveat: DeepSeek's V3 Base foundation model was trained using publicly available internet data that may have included outputs from OpenAI's models and other systems. The base model was also trained using traditional supervised learning methods, meaning some component of its performance may come from conventional training rather than purely from reinforcement learning .

What Are the Real Limitations of This Approach?

Despite impressive benchmark results, DeepSeek's models encountered real-world problems during development. Because the models were trained on both English and Chinese data, early versions produced outputs that mixed the two languages, making them difficult to read. DeepSeek had to add another training stage with language consistency rewards to fix this issue .

This reveals an important truth: even when a model achieves high accuracy on test problems, it may have unexpected flaws that only become apparent through real-world use. The need for iterative refinement suggests that reinforcement learning, while cost-effective, doesn't automatically produce polished, production-ready systems .

The broader takeaway is that DeepSeek's achievement is genuinely impressive from an efficiency standpoint, but the mystery of how these models actually work internally remains unsolved. The models produce correct answers and humanlike explanations, but whether they're truly reasoning or simply pattern-matching in sophisticated ways is still an open question that researchers are actively investigating.