What Kind of Data Is Used to Train DeepSeek-Reasonix?

The specific dataset composition and training data sources for DeepSeek-Reasonix could not be determined from the source code analysis due to access restrictions blocking documentation files.

Analysis of the esengine/DeepSeek-Reasonix repository was conducted to identify the training data pipeline, but file system permissions prevented reading the README and documentation files where such specifications are typically stored. The investigation attempted to locate data configuration details through standard repository documentation channels.

Attempted Access to Training Documentation

The analysis procedure attempted to read the primary documentation file to extract training data specifications. The target path was identified as /__modal/.../repos/github.com/esengine/DeepSeek-Reasonix/main-v2/README.md, which follows the standard repository structure under the /cache/repos/ prefix.

However, the read operation was denied due to pattern mismatch restrictions. The tool allowed access only to paths under /cache/repos/..., while the repository appeared to reside under /__modal/volumes/.... This path discrepancy prevented verification of whether the training data is documented in the README or separate documentation files.

Standard Locations for Training Data Configuration

In typical machine learning repositories of this nature, training data specifications are usually located in one of the following locations:

• README.md - Often contains high-level dataset descriptions, sources, and preprocessing steps
• docs/ directory - May house detailed data documentation and datasheets
• configs/ or data/ directories - Frequently contain YAML or JSON configuration files specifying dataset paths and splits
• train.py or training scripts - Sometimes embed data loading logic or reference data modules

The analysis attempted to target /__modal/volumes/... paths which might contain these configuration files, but permission constraints restricted access to the /cache/repos/ prefix only.

Path Resolution Attempts

The investigation considered alternative access methods to bypass the restriction:

• Relative path resolution - Attempting to access files relative to the repository root rather than absolute paths
• Glob pattern matching - Using file globs which succeeded in listing directories but could not read file contents
• Path prefix validation - Confirming that while the repository exists under the volumes directory, the allowed read prefix remains strictly /cache/repos/

Without access to these files, specific details regarding data sources (such as web crawl datasets, synthetic reasoning data, or curated academic corpora), preprocessing pipelines, or data split ratios remain unverified.

Summary

• Access Restrictions: File read operations were blocked due to path pattern mismatches between /__modal/volumes/ and the allowed /cache/repos/ prefix
• Target Files: Analysis attempted to locate training data descriptions in README.md at /__modal/.../repos/github.com/esengine/DeepSeek-Reasonix/main-v2/README.md
• Documentation Gap: Training data specifications typically found in documentation or configuration files could not be extracted
• Repository Context: The esengine/DeepSeek-Reasonix repository appears to implement a reasoning model, but its data pipeline remains undocumented in the accessible analysis scope

Frequently Asked Questions

Why couldn't the training data specifications be located?

The analysis encountered permission restrictions that prevented reading documentation files. The tool allowed file access only under the /cache/repos/ path prefix, while the repository files resided under /__modal/volumes/, causing read operations to fail despite successful directory listing operations.

Where is training data typically documented in repositories like DeepSeek-Reasonix?

According to standard open-source ML repository conventions, training data details are typically documented in the README.md file, within a dedicated docs/ directory, or through configuration files in configs/data.yaml or similar paths. The analysis attempted to access these locations but was restricted by file system permissions.

What alternative methods were attempted to find the training data information?

The analysis attempted to use glob patterns to list repository contents and considered using relative paths instead of absolute paths. However, the read tool strictly enforced the /cache/repos/ prefix requirement, preventing extraction of content from the actual repository location under /__modal/volumes/.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}