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DeepSeek R1: A New Contender in the AI Arena

DeepSeek R1, a new AI model, uses Chain of Thought reasoning, reinforcement learning, & model distillation for improved accuracy & accessibility. Learn about these key innovations & how smaller models can outperform larger ones.

Shanmukha Karthik

• 4 min read
DeepSeek R1: A New Contender in the AI Arena
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DeepSeek R1 showcases how sophisticated techniques like Chain of Thought reasoning, reinforcement learning, and model distillation can be effectively combined to create potent yet more accessible language models. The model's capacity for self-evaluation, learning from mistakes, and ability to be distilled into smaller, more efficient models signifies a notable step forward in AI research.

The tech world was significantly impacted by the release of DeepSeek R1, a new large language model (LLM) developed by a research team in China. This model has demonstrated impressive capabilities in tackling complex reasoning problems, including math, coding, and scientific reasoning, rivaling the performance of OpenAI's 01 model.

Try Deepseek R1 on Segmind

Let's delve into the core techniques that empower DeepSeek R1:

Chain of Thought Reasoning

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When presented with a math problem, DeepSeek R1 would not only provide the solution, but also detail each step of its reasoning. This would include phrases like, "wait, wait, there's an aha moment," and "let's reevaluate" as it works through the problem. By explicitly showing its work, the model can identify and rectify any errors in its logic before delivering the final answer.

DeepSeek R1 leverages a technique called Chain of Thought (CoT) reasoning to enhance its accuracy. This method involves prompting the model to articulate its thought process step-by-step, essentially "thinking out loud". This detailed reasoning process allows for easier identification of errors in the model's logic. Once an error is identified, the model can be re-prompted to avoid that mistake in subsequent attempts. Instead of simply providing a final answer, the model demonstrates the path it took to reach its solution, complete with self-reflective moments. This meticulous process enables the model to generate more accurate responses.

  • The use of CoT reasoning leads to more accurate responses compared to providing answers without explanation.
  • This technique allows the model to pinpoint exactly where its reasoning went astray so that it can learn from its mistakes.

Reinforcement Learning

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When solving a complex equation, DeepSeek R1 might explore multiple methods to solve it, discovering that one is much more efficient than others. Reinforcement learning allows the model to favor and prioritize the more efficient method as it leads to a higher reward.

DeepSeek R1 uses a distinctive approach to reinforcement learning. Unlike traditional methods where the model is directly provided with correct answers, DeepSeek R1 learns by exploring its environment and optimizing its behavior, or "policy," to maximize a reward. This learning process is analogous to how a baby learns to walk, by trial and error, stumbling, and adjusting its movements until they master the task. The model is not explicitly told what a correct answer is, instead it receives feedback based on how well it performed according to a reward system.

  • The model learns through experimentation, discovering which approaches yield the highest reward.
  • Group relative policy optimization is used to score the model's answers, even when a correct answer is not available for reference. This involves calculating a weighted average of responses based on the old policy and the new policy. This average is multiplied by a standardization value, which measures how much the new policy has improved the reward compared to the average reward.
  • To maintain stability during training, the model's policy changes are carefully controlled using a clipping mechanism. This limits the degree to which the policy can change and prevents drastic, unstable policy shifts, ensuring smoother training.
  • The goal is to adjust the model's policy in such a way that the reward is maximized. This is accomplished by comparing the old and new answers, creating a "min-max" situation where the model minimizes its errors while simultaneously maximizing its reward.
  • Over time, training with reinforcement learning improves the model's accuracy, eventually exceeding the performance of models like OpenAI's 01. By integrating CoT reasoning, the model is able to self-reflect on and evaluate its answers, leading to improved behavior and accuracy.

Model Distillation

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The large DeepSeek R1 model uses Chain of Thought reasoning to demonstrate how to approach and solve complex problems, serving as a teacher to the smaller model. The smaller model learns from these detailed examples and can achieve similar performance without needing the same level of resources.

The full DeepSeek R1 model has 671 billion parameters, making it computationally intensive and requiring significant resources to run. To address this, the researchers employed model distillation to make the model more accessible. In this process, the large DeepSeek R1 model, referred to as the "teacher," trains a smaller model, the "student," on how to reason and answer questions.

  • The teacher model leverages CoT reasoning to generate detailed examples of its thought process and solutions, which are then provided to the student.
  • The student model learns from these examples, attaining similar performance levels as the larger model but with significantly fewer parameters and reduced computational needs.
  • DeepSeek researchers successfully distilled their model into Llama 3 and Qwen.
  • Remarkably, the student model, while being trained through reinforcement learning, has demonstrated the ability to outperform the teacher model, all while utilizing a fraction of the memory and storage. In the experiments documented in the paper, these smaller distilled models were found to outperform larger models such as GPT-4o and Claude 3.5 Sonnet in tasks such as math, coding, and scientific reasoning.

Conclusion

In conclusion, DeepSeek R1 represents a significant advancement in AI, showcasing the power of combining innovative techniques. The model utilizes Chain of Thought reasoning to enhance its accuracy by thinking out loud and self-reflecting. It employs a unique form of reinforcement learning, where it learns by exploring and optimizing its behavior based on rewards, rather than being given the correct answers directly. Finally, model distillation makes the technology more accessible, by training smaller models to perform at the level of the large model but with reduced resources. These smaller models can even outperform the larger ones, marking a significant step forward in the field

Try Deepseek R1 on Segmind