Large language models (LLM) quickly evolve from simple text forecasting systems into advanced reasoning engines capable of making complex challenges. Initially designed to predict the next word in the sentence, these models have now advanced to solving mathematical equations, writing a functional code and making decisions based on data. The development of reasoning techniques is a key driver of this transformation, enabling AI models to process information in a structured and logical way. In this article, he examines the techniques of reasoning of models such as O3 OpenAI, GROK 3Deepseek R1, Google's Gemini 2.0AND SONET CLAUDE 3.7emphasizing their strengths and comparing their performance, costs and scalability.
Reasoning techniques in large language models
To see how these LLM are different, we must first look at the various reasoning techniques that these models use. In this section we present four key reasoning techniques.
- Computing scaling of inference time
This technique improves the modeling of the model by assigning additional computing resources during the response generation phase, without changing the basic structure of the model or its retraining. It allows the model to “think harder”, generating many potential answers, assessing them or improving its initial step. For example, when solving a complex mathematical problem, the model can divide it into smaller parts and act by each of them sequentially. This approach is particularly useful in tasks that require deep, intentional thoughts such as logical puzzles or complicated coding challenges. Although it improves the accuracy of the answer, this technique also leads to higher costs of performing and slower reaction times, thanks to which it is suitable for applications in which precision is more important than the speed. - Learning pure reinforcement (RL)
In this technique, the model is trained to justify by the test and error by rewarding correct answers and punishing errors. The model affects the environment – such as a set of problems or tasks – and learns by adapting your strategies based on feedback. For example, when it is the task of saving the code, the model can test various solutions by winning the prize if the code is successfully made. This approach imitates the way a person learns playing through practice, enabling the model to adapt to new challenges over time. However, pure RL can be calculated and sometimes unstable, because the model can find shortcuts that do not reflect true understanding. - Pure supervised tuning (SFT)
This method increases reasoning by training the model only on high quality data sets, often created by people or stronger models. The model learns to replicate correct reasoning patterns of these examples, making it efficient and stable. For example, to improve your ability to solve equations, the model can examine a set of solved problems, learning to follow the same steps. This approach is simple and profitable, but it is largely based on data quality. If the examples are weak or limited, the model's performance may suffer and can fight tasks outside the scope of training. Pure SFT is best suited for well -defined problems in which clear, reliable examples are available. - Strengthening learning with supervised tuning (RL+SFT)
This approach combines the stability of the supervised tuning with the possibility of adapting the learning learning. The models first undergo supervised training on label data sets, which provides a solid foundation of knowledge. Then learning to strengthen helps improve the skills of solving model problems. This hybrid method balances stability and adaptability, offering effective solutions for complex tasks, while reducing the risk of irregular behavior. However, this requires more resources than pure supervised tuning.
Reasoning approach in leading LLMS
Now let's examine how these reasoning techniques are used in leading LLM, including O3 OpenAI, Grok 3, Deepseek R1, Google's Gemini 2.0 and Claude 3.7 Sonnet.
- O3 OpenAI
O3 Openai mainly uses calculation scaling during application to improve its reasoning. By devoting additional calculation resources when generating answers, O3 is able to provide very accurate results of complex tasks, such as advanced mathematics and coding. This approach allows o3 to work exceptionally well on comparative tests such as ARC-AGI test. However, this is due to the costs of higher costs and slower reaction times, thanks to which it is best suited for applications in which precision is crucial, such as research or technical problem solving. - Xia's Gook 3
GROK 3, developed by XAI, combines scaling of application time calculations with specialized equipment, such as co -created for tasks such as symbolic mathematical manipulation. This unique architecture allows groc 3 quick and accurate processing of large amounts of data, which makes it very effective in real -time applications, such as financial analysis and live data processing. While Grok 3 offers fast performance, its high calculation requirements may increase costs. In the environment, it leads in environments where speed and accuracy are the most important. - Deepseek R1
Deepseek R1 initially uses learning pure reinforcement to train its model, enabling the development of independent strategies for solving problems through test and errors. This makes the Deepseek R1 flexible and is able to handle unknown tasks, such as complex mathematical challenges or coding. However, Pure RL can lead to unpredictable results, so Deepseek R1 contains supervised tuning in later stages to improve consistency and consistency. This hybrid approach makes the Deepseek R1 a profitable choice for applications that prioritize flexibility compared to polished answers. - Google's Gemini 2.0
Google's Gemini 2.0 uses a hybrid approach, probably combining scaling of application calculations with reinforcement learning to improve its reasoning. This model has been designed to support multimodal inputs, such as text, paintings and sound, at the same time perfect in real -time reasoning tasks. His ability to process information before the answer ensures high accuracy, especially in complex queries. However, like other models using the scaling of inference time, Gemini 2.0 can be expensive to use. It is ideal for applications requiring reasoning and multimodal understanding, such as interactive assistants or data analysis tools. - SONET CLAUDE 3.7 Anthropic
Claude 3.7 Sonet with Anthropic integrates scaling of application calculations with particular emphasis on safety and equalization. This enables the model to do good tasks that require both accuracy and explanation, such as financial analysis or review of legal documents. His mode of “extended thinking” allows him to adapt the efforts of reasoning, which makes him versatile both for fast and thorough problem solving. Although it offers flexibility, users must manage a compromise between the reaction time and the depth of reasoning. The Claude 3.7 sonnet is particularly suitable for regulated industries in which transparency and reliability are crucial.
Lower line
The transition from basic language models to sophisticated reasoning systems is a serious jump in AI technology. Using techniques such as calculating scaling of inference time, pure reinforcement learning, RL+SFT and Pure SFT, models such as O3 Openai, Grok 3, Deepseek R1, Google's Gemini 2.0 and Claude 3.7 Sonnet have become more expert in solving complex problems with real problems. The approach of each model to reasoning determines its strengths, from deliberate solving O3 problems to the profitable elasticity of Deepek R1. As these models evolve, they will unlock new possibilities of artificial intelligence, which makes it an even stronger tool for solving real challenges.
