yakuraku/refined-reasoning-dataset

Refined Reasoning Dataset

High-quality reasoning dataset refined for fine-tuning language models on complex reasoning tasks.

Dataset Summary

This dataset contains 10,277 examples of reasoning-intensive prompts and responses, available in two formats for supervised fine-tuning (SFT) of instruction-tuned language models.

• Total Examples: 10,277
• Training Split: 9,777 examples (95%) / 9,650 examples (ShareGPT - filtered)
• Validation Split: 500 examples (5%) / 492 examples (ShareGPT - filtered)
• Formats:
  • Tunix/Gemma Format: train.jsonl, val.jsonl
  • ShareGPT Format: train_sharegpt.jsonl, val_sharegpt.jsonl
• Total Size: ~99 MB (both formats combined)
• Templates:
  • Gemma 2 chat format (<start_of_turn>user\n...<end_of_turn>\n<start_of_turn>model\n...<end_of_turn>)
  • ShareGPT format (conversational format with system/human/gpt roles)

Dataset Structure

Available Formats

This dataset is provided in two formats to support different training frameworks:

1. Tunix/Gemma Format (train.jsonl, val.jsonl)

Each example is a JSON object with the following structure:

{
  "text": "<start_of_turn>user\n[PROMPT]<end_of_turn>\n<start_of_turn>model\n[REASONING + ANSWER]<end_of_turn>",
  "token_count": 1234,
  "meta_category": "Math",
  "meta_difficulty": "Medium",
  "meta_quality": 4,
  "meta_verifiable": true,
  "meta_tags": ["algebra", "reasoning"],
  "refined": true,
  "original_reasoning_length": 500,
  "new_reasoning_length": 300
}

2. ShareGPT Format (train_sharegpt.jsonl, val_sharegpt.jsonl)

Optimized for Unsloth and other training frameworks. Each example follows the conversational structure:

{
  "conversations": [
    {
      "from": "system",
      "value": "You are a logical reasoning assistant. You must always use <reasoning> tags to think through the problem step-by-step before providing the final <answer>."
    },
    {
      "from": "human",
      "value": "[PROMPT]"
    },
    {
      "from": "gpt",
      "value": "<reasoning>\n[STEP-BY-STEP REASONING]\n</reasoning>\n<answer>\n[FINAL ANSWER]\n</answer>"
    }
  ]
}

Key Features of ShareGPT Format:

• ✅ Clean separation of user input and model output
• ✅ System prompt included to reinforce reasoning structure
• ✅ Preserves all <reasoning>, <summary>, and <answer> tags
• ✅ Filtered for quality (removed 1-1.4% broken tags, 0.2-0.3% length outliers)
• ✅ Ready for direct use with Unsloth's ShareGPT loader

Data Fields

Field	Type	Description
text	string	Full formatted conversation with Gemma 2 chat template
token_count	integer	Approximate token count
meta_category	string	Category (Math, Coding, Science, etc.)
meta_difficulty	string	Difficulty level (Easy, Medium, Hard)
meta_quality	integer	Quality score (1-5)
meta_verifiable	boolean	Whether answer is objectively verifiable
meta_tags	list[string]	Associated tags
refined	boolean	Whether example underwent refinement
original_reasoning_length	integer	Original reasoning token count
new_reasoning_length	integer	Refined reasoning token count

Data Splits

Tunix/Gemma Format

Split	Examples	Size	File
train	9,777	48 MB	train.jsonl
validation	500	2.5 MB	val.jsonl

ShareGPT Format (Quality Filtered)

Split	Examples	Size	File
train	9,650	46 MB	train_sharegpt.jsonl
validation	492	2.4 MB	val_sharegpt.jsonl

Note: ShareGPT format has slightly fewer examples due to quality filtering (removed entries with broken reasoning tags and extreme length outliers).

Usage

Loading the Dataset

Option 1: Tunix/Gemma Format

from datasets import load_dataset

# Load the Tunix format dataset
dataset = load_dataset("yakuraku/refined-reasoning-dataset", data_files={
    "train": "train.jsonl",
    "validation": "val.jsonl"
})

# Access splits
train_data = dataset["train"]
val_data = dataset["validation"]

# View example
print(train_data[0])

Option 2: ShareGPT Format (Recommended for Unsloth)

from datasets import load_dataset

# Load the ShareGPT format dataset
dataset = load_dataset("yakuraku/refined-reasoning-dataset", data_files={
    "train": "train_sharegpt.jsonl",
    "validation": "val_sharegpt.jsonl"
})

# Access splits
train_data = dataset["train"]
val_data = dataset["validation"]

# View example
example = train_data[0]
print(example["conversations"])

Example Use Case: Fine-tuning with Unsloth

from datasets import load_dataset
from unsloth import FastLanguageModel

# Load ShareGPT format dataset
dataset = load_dataset("yakuraku/refined-reasoning-dataset", data_files={
    "train": "train_sharegpt.jsonl",
    "validation": "val_sharegpt.jsonl"
})

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen2.5-7B-Instruct",
    max_seq_length=2048,  # Based on dataset analysis (95th percentile)
    load_in_4bit=True,
)

# The ShareGPT format is directly compatible with Unsloth
# No additional formatting needed!

Example Use Case: Fine-tuning Gemma 2 2B (Tunix Format)

from datasets import load_dataset

# Load Tunix format dataset
dataset = load_dataset("yakuraku/refined-reasoning-dataset", data_files={
    "train": "train.jsonl",
    "validation": "val.jsonl"
})

# The 'text' field is already formatted with Gemma 2 chat template
# Ready for direct use in SFT training

for example in dataset["train"]:
    # example["text"] contains full formatted conversation
    # No additional templating needed
    formatted_text = example["text"]

Dataset Creation

Source Data

Created from multiple high-quality reasoning datasets, including:

• Mathematical reasoning tasks
• Coding challenges
• Scientific reasoning
• General problem-solving

Data Refinement

Examples underwent quality refinement:

• ✅ Reasoning coherence verification
• ✅ Answer accuracy validation
• ✅ Formatting standardization
• ✅ Duplicate removal
• ✅ Quality filtering (minimum score: 3/5)

Annotations

Metadata annotations include:

• Category classification: Automated categorization by domain
• Difficulty assessment: Based on reasoning complexity
• Quality scoring: Combined manual and automated quality checks
• Verifiability tagging: Whether answers can be objectively verified

Intended Use

This dataset is designed for:

• ✅ Supervised fine-tuning (SFT) of instruction-tuned models
• ✅ Reasoning capability enhancement
• ✅ Multi-step problem-solving training
• ✅ Academic and research purposes

Limitations

• Dataset may contain biases from source data
• Reasoning quality varies across examples
• Some examples may contain outdated information
• Not all reasoning paths are optimal
• Not suitable for production systems without validation

License

MIT License

Citation

If you use this dataset, please cite:

@dataset{refined_reasoning_dataset_2025,
  title={Refined Reasoning Dataset},
  author={Yash},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/YOUR-USERNAME/refined-reasoning-dataset}
}