README.md

metadata

license: agpl-3.0
license_name: sspl-1.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
dataset_info:
  features:
    - name: messages
      list:
        - name: role
          dtype: string
        - name: content
          dtype: string
    - name: metadata
      struct:
        - name: topic_path
          list: string
        - name: checker_score
          dtype: int64
  splits:
    - name: train
      num_bytes: 135701582
      num_examples: 18098
    - name: validation
      num_bytes: 16079590
      num_examples: 2012
  download_size: 151672373
  dataset_size: 151781172

pythink-20k

High-quality Python reasoning traces for training rigorous, step-by-step thinking.

pythink-20k is a quality-controlled, synthetic, curated dataset of 20,000 executable Python reasoning plans. Each example teaches a model to break down complex problems into structured, verifiable steps using a consistent execution trace style.

This is an initial release focused on getting an initial dataset out for testing and experimentation. I expect that the quality is pretty good, but quality is currently limited by the ability for existing models to represent thoughts as Python code.

Why?

Current LLMs have very unstructured, undisciplined thoughts which are poorly comprehensible and often inefficient. Using a programming language for thinking makes thoughts easier to audit, more structured, and more consistent.

Why Python?

• LLMs are excellent at Python already
• Python is more token-efficient than JSON, typescript, or other languages

What’s in the Data?

Each example contains a full Python-based reasoning trace that:

• Uses short, action-oriented # Next: comments
• Performs actual modeling, calculation, validation, or simulation in code
• Stays in "plan mode" (does not output raw implementation code in other languages)
• Ends with a clean finalAnswer

The training data treats the entire reasoning trace as its response. This dataset will NOT train a model to answer the question. The whole point is to train the model to consider how the question should be thought about, instead of rushing to reach a conclusion.

The outputs are generally executable Python code, meaning than many of the responses can be executed to get the real output.

Example Format

{
  "messages": [
    {
      "role": "user",
      "content": "Design a system that programmatically generates a TLS 2048-bit RSA certificate with 90-day validity..."
    },
    {
      "role": "assistant",
      "content": "# Next: validate RSA certificate requirements...\nrsaKeyBits = 2048\nvalidityDays = 90\n..."
    }
  ],
  "metadata": {
    "topic_path": ["coding", "Network Servers and Daemons", "TLS/SSL Termination"],
    "checker_score": 10
  }
}

Key Features

• 20k high-quality traces (filtered with automated quality gate)
• Strong emphasis on executable reasoning rather than descriptive text
• Diverse domains: mathematics, coding, physics, philosophy, biology, and more
• Includes rich metadata (checker_score, topic path)
• Clean chat format ready for SFT

Intended Use

This dataset is designed for:

• Supervised fine-tuning of reasoning models
• Training models to produce structured, verifiable reasoning traces
• Research on executable chain-of-thought and code-as-reasoning
• Building the foundation for more advanced agentic and memory-augmented systems

Data Quality

All examples passed a multi-criteria quality filter including:

• Minimum checker score
• Presence of executable steps and validation logic
• Avoidance of target-language code leakage
• Reasonable length and structure

Limitations

• Some traces may still contain minor stylistic inconsistencies.
• Focused on reasoning structure rather than factual correctness.

License

AGPL-3.0

Citation

If you use this dataset, please cite:

@misc{pythink-20k,
  author = {NuclearManD},
  title  = {pythink-20k: High-Quality Python Reasoning Traces},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/NuclearManD/pythink-20k}}
}

Future Releases

Planned improvements include:

• Higher quality filtering and human review
• Preference data (chosen vs rejected reasoning traces)
• Multi-turn and agentic reasoning examples

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Built as part of ongoing work on rigorous reasoning systems.