Deepseek AI introduces the Code/O: a new approach that transforms reasoning patterns based on codes into natural language formats to improve LLMS reasoning capabilities

Large language models (LLM) significantly advanced natural language processing, but reasoning stays a lasting challenge. While tasks, reminiscent of mathematical problem solving and generating code, use structural training data, wider reasoning tasks-as logical deduction, scientific inference and symbolic reasoning-from rare and fragmentary data. Traditional approaches, reminiscent of constant claim to code, often set reasoning signals, which hinders generalization models. Even the approach to generating text to code remain limited by learning specific to syntax, limiting their use outside of programming tasks. A more structured approach to the disclosure of LLM is required to basic reasoning patterns while maintaining logical rigorous.

Deepseek AI Research presents Codei/OThe approach that transforms the reasoning based on the code into a natural language. Transforming the raw code into the format of the input forecast and expressing the steps of reasoning Presented (COT) justification (COT)Codei/O allows LLM internalization of basic reasoning processes reminiscent of Logical flow planning, decision -making and modular distribution. Unlike conventional methods, Codei/O separates reasoning from code syntax, enabling wider use while maintaining the logical structure.

Technical review and advantages

Codei/O is in step with the structured data processing pipeline:

1. Collecting raw code files: Over 450,000 functions were collected from many sources, including repositories of algorithms and academic data sets.
2. Data standardization: The collected code was improved using Deepseek-V2.5, ensuring transparency and compatibility of performance.
3. Generating input pairs: The functions were performed with various outlays to create structured examples of coaching in various tasks of reasoning.
4. Generating chain reason: Using models reminiscent of Deepseek-V2.5, the natural language explanations were created to ensure structured reasoning.
5. Verification and improvement: The forecasts were approved by execution, and incorrect answers were modified iteratively to improve the accuracy of reasoning.

Key functions of Codei/O:

• Transformational science: Transforms various code patterns in Cot in natural languagemaking the reasoning of transfer outside programming contexts.
• Learning syntax with decoration: Separates logical reasoning from Code syntaxIt improves adaptive ability in various tasks of reasoning.
• Multi -purpose improvement: Increases performance Symbolic, scientific, logical, mathematical and reasonable reasonable domains.
• Verifiability: Forecasts will be kept through Buffed fit or re -issuing of ground truth.
• Iterative improvement: Refined version, Codei/O ++, employs Ambiguous version To increase the accuracy of reasoning.

Empirical results and performance

The influence of codei/O was tested Four basic models (from 7b to 30b parameters) 14 reasoning of comparative tests Taking logic, symbolic inference, mathematics, scientific deduction and reasonable reasoning.

Arrangements:

• Consistent improvements: Code training/O led Higher ends in the field of comparative tests compared to traditional pretruary methods.
• Generalization in tasks: Unlike existing approaches, which improve specific tasks, but degrade performance elsewhere, Codei/O showing sustainable improvements.
• Comparison with the basics: CODE/O exceeded data sets reminiscent of OpenMathinstruct2, OpenCoder-Sft-Stage1 and Internet.
• The effectiveness of multi -purpose sophistication: Codei/O ++ Further improved results through iteratively improving incorrect answers, using feedback for higher quality reasoning.

For example, in logical and symbolic test reason, reminiscent of BBH and CruxevalCodei/O led to significant performance advantages. IN Mathematical reasoning tasks (GSM8K, Math and MMLUM)He showed improvements in relation to existing base lines. Even in Health reasoningIn case of code -based methods normally fight, Kodei/O maintained solid results.

Application

Codei/O presents a structured way to increase LLMS reasoning by utilizing input transformations from the code in the real world. Instead of focusing on isolated tasks of reasoning, he extracts universal reasoning patterns and explains them to natural language explanations. This structured approach to learning ensures that models acquire solid reasoning skills in various domains.

Introduction Version with many phrases (CODE/O ++) Further improving the accuracy of reasoning, which shows that iterative learning from the implementation of feedback increases the reliability of the model. Making forecasts verifiableCodei/O provides a scalable and reliable approach to improving LLM reasoning.

By brosting Language reasoning based on codes and naturalCodei/O offers a promising direction of LLM cognitive improving as well as to programming tasks.

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