guell00/Astra-COT-40K

High Quality Reasoning Chat Dataset

A curated conversational dataset designed for instruction tuning and reasoning-focused language model training.

The corpus contains high-quality multi-turn conversations, detailed explanations, coding examples, analytical tasks, and explicit reasoning traces (<think>...</think>). The dataset was normalized into a standard chat format and filtered to remove duplicate conversations while preserving the original responses and reasoning behavior.

Key Characteristics

• 40,924 unique conversations
• Multi-turn chat format
• Explicit reasoning traces preserved
• High diversity of tasks and domains
• Suitable for SFT and reasoning training
• Compatible with modern chat templates and fine-tuning frameworks

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Data Quality

The dataset was manually inspected and sampled across thousands of examples. The corpus exhibits:

✅ Long-form and detailed answers

✅ Strong instruction-following behavior

✅ Multi-domain coverage

✅ High reasoning density

✅ Low duplication rate

✅ Consistent assistant formatting

Many responses contain structured reasoning before the final answer, making the dataset particularly useful for training models that need intermediate reasoning capabilities.

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Approximate Topic Distribution

The percentages below are approximate and based on sampling and categorization of the conversations.

Category	Percentage
General Knowledge & Education	22%
Programming & Software Engineering	21%
Reasoning & Problem Solving	17%
Mathematics & Logic	11%
Writing & Text Generation	9%
Science & Technology	7%
Roleplay & Creative Tasks	5%
Translation & Languages	4%
Business & Productivity	2%
Miscellaneous	2%

---

Reasoning Distribution

Type	Percentage
Explicit <think> reasoning	~63%
Direct answer without reasoning	~37%

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Conversation Length Distribution

Messages per conversation	Percentage
2-3 messages	48%
4-6 messages	33%
7-10 messages	14%
10+ messages	5%

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Response Length Distribution

Assistant Response Length	Percentage
Short (<150 tokens)	24%
Medium (150-500 tokens)	51%
Long (>500 tokens)	25%

The dataset intentionally favors medium and long responses to encourage richer reasoning and instruction-following behavior.

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Data Format

Each line is a single JSON object:

{
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "Explain how transformers work."
    },
    {
      "role": "assistant",
      "content": "<think>Need to explain attention first...</think>\nTransformers are neural networks..."
    }
  ]
}

Supported roles:

• system
• user
• assistant

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Intended Uses

This dataset is suitable for:

• Supervised Fine-Tuning (SFT)
• Chain-of-Thought training
• Reasoning models
• Instruction-following assistants
• Distillation research
• Chatbot development
• Alignment experiments
• Long-form response generation

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Example Usage

from datasets import load_dataset

dataset = load_dataset(
    "YOUR_USERNAME/high-quality-reasoning-chat-dataset",
    split="train"
)

sample = dataset[0]
print(sample["messages"])

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Training Example

text = tokenizer.apply_chat_template(
    sample["messages"],
    tokenize=False,
    add_generation_prompt=False
)

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Limitations

• Contains synthetic and distilled conversations.
• Reasoning traces may not always represent optimal reasoning strategies.
• The dataset is intended for research and model training purposes and should not be used as a factual benchmark.

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Citation

@misc{guell00_astra_cot_40k,
  author = {guell00},
  title  = {Astra-COT-40K},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/guell00/Astra-COT-40K}},
  note   = {High-quality reasoning and instruction-following conversational dataset for supervised fine-tuning.}
}