Revealing DeepSeek R1 Reasoning Secrets: An Easy–to–Understand Guide to Building Top–Tier Reasoning Models

I. Core Question: Why is DeepSeek R1 So Powerful in Reasoning? The Key Lies in These 3 Points

DeepSeek R1’s reasoning ability is comparable to that of OpenAI o1–1217, and the core secrets are hidden in DeepSeek R1 reasoning secrets. Simply put, there are three key operations: not relying on massive annotated data, directly training the base model with reinforcement learning, then polishing it through multi–stage optimization, and finally enabling small models to possess strong reasoning capabilities through distillation technology. Unlike traditional models, it does not require humans to first annotate a large amount of “question–correct answer” data; instead, it allows the model to explore problem–solving ideas on its own during training, which is equivalent to enabling the model to evolve through “independent thinking.”

II. Initial Exploration of DeepSeek R1 Reasoning Secrets: How Does Pure Reinforcement Learning Make Models Smarter?

The most striking operation in DeepSeek R1 reasoning secrets is skipping the “supervised fine–tuning” step—without humans pre–annotating the correct reasoning process, directly using the base model (DeepSeek–V3–Base) for reinforcement learning. To make training more efficient and cost–effective, the team adopted a method called “Group Relative Policy Optimization (GRPO).” The specific logic is simple: for the same question, let the model generate multiple answers, then score and optimize by comparing the quality of these answers, without the need to additionally train a specialized “referee model.” This not only ensures effectiveness but also saves a great deal of computing power costs.

To make the model smarter as it trains, there must be clear “reward rules,” just like teaching a child—praise for doing well and reminders for making mistakes. The team designed two core reward rules and deliberately avoided the problematic “neural reward model” (simply put, using AI to judge the quality of answers). This is because they found that such an AI referee would make the model “take shortcuts”—for example, only pursuing correct formatting regardless of whether the answer is wrong, which is commonly referred to as “reward hacking.” Moreover, retraining the AI referee is both expensive and time–consuming. The specific reward rules are as follows:

A simple training template is also indispensable. The team only required the model to follow the structural requirement of “outputting the reasoning process first and then the answer,” without adding any content–oriented biases, so as to observe the model’s natural evolution during the reinforcement learning process. This “minimum intervention” training idea allows the model to independently explore ideas for solving complex problems, laying the foundation for the subsequent emergence of reasoning capabilities.

III. From Zero to R1: How Does Multi–Stage Optimization Improve Reasoning Capabilities?

The development of DeepSeek R1 was not achieved in one step but went through iterative optimization from DeepSeek–R1–Zero to DeepSeek–R1. This iterative process also supplements the complete picture of DeepSeek R1 reasoning secrets. As a preliminary attempt at pure reinforcement learning, DeepSeek–R1–Zero successfully achieved a significant improvement in reasoning capabilities—the pass@1 score on AIME 2024 soared from 15.6% to 71.0%, and reached 86.7% under majority voting, comparable to OpenAI o1–0912. However, it also had problems such as poor readability and language mixing, making it difficult to meet practical application needs.

To solve these problems and further improve performance, the team designed a four–stage training pipeline to build DeepSeek R1. The first stage is cold start: collecting thousands of high–quality long Chain–of–Thought (CoT) data to fine–tune the base model. These data are manually optimized to ensure readability, and a standardized format of “reasoning process + summary” is designed to improve output quality from the source. The second stage is reasoning–oriented reinforcement learning: on the basis of the model fine–tuned during the cold start, continue to apply large–scale reinforcement learning, and add a language consistency reward. Although this sacrifices a small amount of performance, it greatly improves readability, making the model more in line with human usage habits.

The third stage is rejection sampling and supervised fine–tuning: when the reasoning–oriented reinforcement learning converges, use the checkpoint to generate reasoning data, and at the same time supplement non–reasoning domain data such as writing and factual QA, with a total of about 800,000 samples to fine–tune the model, comprehensively improving the model’s general capabilities. The fourth stage is full–scenario reinforcement learning: combining rule–based rewards (for reasoning tasks) and reward models (for general tasks) to balance reasoning performance and human preferences, and finally form a comprehensively capable DeepSeek R1.

IV. Can Small Models Also Have Strong Capabilities? What is the Key Role of Distillation Technology?

DeepSeek R1 reasoning secrets not only include the model’s own training strategies but also cover an efficient transfer scheme for reasoning capabilities—distillation technology. The team found that directly using DeepSeek R1’s reasoning data to fine–tune open–source small models (such as Qwen2.5 and Llama3 series) is far more effective than directly performing reinforcement learning on small models. This finding proves that the reasoning patterns explored by large models are crucial for improving the performance of small models.

Experimental data fully verify the effectiveness of distillation technology: the 7B distillation model based on Qwen2.5 achieved a pass@1 score of 55.5% on AIME 2024, exceeding QwQ–32B–Preview; the 32B distillation model performed even more impressively, with a score of 72.6% on AIME 2024, 94.3% on MATH–500, approaching the level of o1–mini. The team open–sourced multiple distillation models ranging from 1.5B to 70B, allowing more researchers and developers to use high–quality reasoning models at low cost, which further releases the value of DeepSeek R1 reasoning secrets.

V. Performance Verification: How Strong is DeepSeek R1’s Reasoning Ability?

Comprehensive benchmark tests have verified the effectiveness of DeepSeek R1 reasoning secrets from multiple dimensions. In math tasks, DeepSeek R1 achieved a pass@1 score of 79.8% on AIME 2024, slightly exceeding OpenAI o1–1217; it also obtained an impressive score of 97.3% on MATH–500, on par with o1–1217. In coding tasks, its Codeforces rating reached 2029, surpassing 96.3% of human participants, demonstrating expert–level coding competitiveness.

In terms of knowledge–based tasks, scores of 90.8% on MMLU, 84.0% on MMLU–Pro, and 71.5% on GPQA Diamond significantly exceeded DeepSeek–V3. Although slightly lower than o1–1217, it outperformed other closed–source models. In open–domain generation tasks, the length–controlled win rate on AlpacaEval 2.0 was 87.6%, and the win rate on ArenaHard was 92.3%, proving that it not only has strong reasoning capabilities but also excellent general interaction capabilities. These data together constitute strong evidence of DeepSeek R1’s reasoning ability and verify the feasibility of its core secrets.

VI. What Are the Insights and Future Directions of DeepSeek R1 Reasoning Secrets?

The research and development process of DeepSeek R1 provides important insights for improving the reasoning capabilities of large models: pure reinforcement learning can independently foster the model’s reasoning capabilities, multi–stage optimization can balance performance and usability, and distillation technology realizes the efficient downward transfer of reasoning capabilities. These core elements that constitute DeepSeek R1 reasoning secrets point out the direction for subsequent research.

In the future, the team will focus on four directions to improve the model: first, enhance general capabilities and explore the application of long CoT in fields such as function calling and multi–turn dialogue; second, solve the problem of language mixing and optimize the processing capabilities of non–Chinese and non–English languages; third, optimize prompt engineering to reduce the model’s sensitivity to prompts; fourth, strengthen the performance of software engineering tasks and improve training efficiency through rejection sampling and asynchronous evaluation. With the solution of these problems, DeepSeek R1 reasoning secrets will be further enriched, providing support for the continuous breakthrough of large models’ reasoning capabilities.

Overall, the success of DeepSeek R1 is not accidental but the result of the joint action of its core reasoning secrets—driven by pure reinforcement learning, multi–stage iterative optimization, and empowered by distillation technology. These innovative strategies not only create a high–performance reasoning model but also provide a referenceable training paradigm for the entire industry, promoting the development of large models’ reasoning capabilities towards higher efficiency and universality.