Deepseek AI launches Codei / O: The novel method that converts imaginary patterns based on the code into natural language formats to develop llms consultation formats

Large models of language (llms) have improved significantly in natural language, but consultation is always a challenging challenge. Ngenkathi imisebenzi efana nokuxazululwa kwezinkinga zezibalo kanye nokuhlolisisa ikhodi kusuka kudatha yokuqeqeshwa ehlelekile, imisebenzi ebanzi yokubonisana – njengokuncishiswa okunengqondo, ukuncishiswa kwesayensi, kanye nokubonisana okungokomfanekiso – kuhlushwa idatha ye-sparse. Traditional methods, such as continuously in the Code, often embark on well-consulted signals, making it difficult for models to normal. Even text-to-code refinements are always enforced the special Syntax readiness, reduces their performance over programs related to programs. A more organized way is needed to expose the llms in the basic consultation patterns while keeping logical stability.

Deepseek Assemak Ai Codei / oThe method that changes the thinking based thinking into the natural language. By converting the green code into the installation format and expressing counterbalancing steps Chain-of-tempent (COT) RatesThe code / o allows llms to act in traditional reform processes such as Logic Flow, decision to take thumb, and stomach deterioration. Unlike common ways, codei / O divides the contents from the synntax of the code, making a comprehensive installation of the logical structure.

Technological Views and Benefits

Codei / O Follows the correct pipe for data processing:

1. To collect raw code files: More than 450k activities were collected from many sources, including algorithm reposingories and instruction planning details.
2. To stop the information: Collected code was refined using Deepseek-V2.5, confirms the clarification and functionality.
3. It is done in pairs of installation: Jobs are made of a variety of installation to create examples of formal training in various thinking activities.
4. Carrying Chain-of-Consideration: Models are used with Deepseek-V2.5, the definition of environmental language explanations were produced to provide for organized reasons.
5. Verification and analysis: Predicted predictions were verified by doing, with the wrong answers to review the Iteratively to improve thoughtful accuracy.

Important Features of CodeI / O:

• Variable reading: Converts different code patterns to Natural language Cot LanguageTo make thinking transfers more than systems.
• Syntax re-reading: Separates logical thinking from The syntax of the codepromoting flexibility in reasoning activities.
• Multiple job development: Improves working across Symbolic, scientific, logical, mathematical, and domains of the camponense.
• Verification: Forecasts can be verified True True Correspondence – Comparing or Recycling.
• Iterative refinement: Refined Type, Codei / O ++, employed Various Reviews develop the accuracy of thinking.

Powerful Results and Working

The impact of the CodeI / O was tested across Four Basic Models (From from 7b parameters to 30b) 14 signs of consultation Covering Logic, a symbolic verification, mathematical, scientific reduction, and the public consultation.

Found:

• Fixed Development: Codei / O Training Leaded Supreme scores across the consultation of benches compared to traditional rest.
• Normal in Works: Unlike existing methods that promote certain tasks but damage the functionality, the code / o indicate moderate enhancements.
• Compared to the basics: Codei / o Protected Datisets as Openmathinsruct2, Opencoder-Sort-Stage1, as well as Bixstruct.
• The effectiveness of many refinements: Conolesti / O ++ Other advanced results by analysis of wrong answers, the improvement of better quality murder.

For example, in logical and symbolic benchmarks like Bbh and cruxevalCode / O has resulted in visualization of the benefit. In Math Reasoning With Works (GSM8K, Math, MMLU-Stem)indicate the development of existing foundations. I'm like a Commonce consultationWhen code code is usually available, codii / O keeps strong results.

Store

Coloi / o produces a systematic way to improve llms by installing input transfers from the original world code. Instead of focusing on the solo work of the solitude, the universe issued the universe and translated Definitions of Natural Language language. This system of orderly learning guarantees that models acquire strong thinking skills in all domains.

The introduction of Various Review (CodeEI / O ++) In addition emphasizes the accuracy that shows accuracy, indicating that learning from the killing of ACCEBACK is improving the trust of the model. By making predictions -UThe – sureCodei / O provides a manner that has a relationship and reliable to improve the LLM consultation.

By tying Reasoning in the code based on the code and the environmentThe Codei / O is providing a promising index for the development of llMs comprehension of the program related to the program.

Survey Page and GitHub paper. All credit for this study goes to research for this project. Also, feel free to follow it Sane and don't forget to join ours 75k + ml subreddit.

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