DeepSeek R1: Principles and Effects

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DeepSeek-R1 is an advanced reasoning model developed to enhance the logical analysis and decision-making capabilities of large language models (LLMs). It represents a comprehensive technical pathway from a general-purpose foundation to specialized reasoning, alongside models like V3 and R1-Zero. This article aims to clarify the relationships among these models through flowchart illustrations, comparative analyses, and breakdowns of training logic.

DeepSeek-R1-Zero: Pure Reinforcement Learning Approach

DeepSeek-R1-Zero employs a pure reinforcement learning (RL) strategy, foregoing supervised fine-tuning (SFT). This approach validates the feasibility of enhancing reasoning capabilities through RL alone. The model undergoes multiple RL training stages, during which it autonomously develops reasoning abilities. Notably, after thousands of RL steps, DeepSeek-R1-Zero demonstrates significant performance improvements in reasoning benchmarks. For instance, its pass@1 score on the AIME 2024 benchmark increased from 15.6% to 71.0%, and further to 86.7% with majority voting, matching the performance of OpenAI's o1-0912 model. However, challenges such as poor readability and language mixing persist.


DeepSeek-R1: Integrating Supervised Fine-Tuning and Reinforcement Learning

To address the limitations of DeepSeek-R1-Zero, the DeepSeek-R1 model integrates a small amount of high-quality data for cold start and employs a multi-stage training process:

  1. Supervised Fine-Tuning (SFT): Utilizing several thousand cold start data points to fine-tune the base model, enhancing its initial performance.
  2. Reinforcement Learning (RL): Applying RL techniques to further refine the model's reasoning capabilities.
  3. Second SFT: Using rejection sampling to create new SFT data, combining reasoning and non-reasoning tasks, and retraining the base model.
  4. Final RL Stage: Conducting additional RL training, considering prompts from various scenarios.

This iterative process results in a model that not only matches the performance of OpenAI's o1-1217 but also produces clear and coherent chains of thought.


Key Innovations in DeepSeek-R1

  • Group Relative Policy Optimization (GRPO): An RL algorithm that enhances the model's reasoning performance by optimizing policy updates.
  • Format Rewards: Incentives designed to improve the readability and structure of the model's outputs.
  • Training Templates: Structured formats that guide the model during training, ensuring consistency and clarity in responses.

These innovations collectively contribute to the model's ability to perform complex reasoning tasks effectively.


DeepSeek-R1 exemplifies a significant advancement in enhancing the reasoning capabilities of large language models. By integrating supervised fine-tuning with reinforcement learning and introducing novel training methodologies, it addresses previous challenges and sets a new standard in AI-driven reasoning.

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