Medical-Reasoning-SFT-Mega

The ultimate medical reasoning dataset - combining 7 state-of-the-art AI models with fair distribution deduplication. 1.79 million unique samples with 3.78 billion tokens of medical chain-of-thought reasoning.

Dataset Overview

Metric	Value
Total Samples	1,789,998 (after deduplication)
Total Tokens	~3.78 Billion
Content Tokens	~2.22 Billion
Reasoning Tokens	~1.56 Billion
Samples with Reasoning	1,789,764 (100.0%)
Unique Questions	1,237,711
Shared Questions	552,287
Source Models	7
Language	English

Source Models

This dataset combines reasoning from 7 leading AI models with fair representation:

Model	Original	Unique	From Shared	Final	%
MiniMax-M2.1	204,773	76,892	42,126	119,018	6.6%
Baichuan-M3-235B	124,520	30,589	43,070	73,659	4.1%
GPT-OSS-120B	506,150	403,208	42,113	445,321	24.9%
Qwen3-Next-80B	604,249	295,828	118,539	414,367	23.1%
GLM_4.5_Air	225,179	151,278	42,117	193,395	10.8%
Nemotron-Nano-30B	444,544	86,978	118,546	205,524	11.5%
Trinity-Mini	810,284	192,938	145,776	338,714	18.9%

Total before deduplication: 2,919,699 samples Total after deduplication: 1,789,998 samples

Deduplication Strategy: Fair Distribution

When the same medical question appears in multiple source datasets, we use fair distribution to ensure balanced representation:

1. Unique questions (1,237,711): Questions appearing in only one model are included directly
2. Shared questions (552,287): Questions appearing in multiple models are distributed fairly across all contributing models

For shared questions, we assign each question to the model that has been assigned the fewest shared questions so far. This ensures no single model dominates the duplicate pool, resulting in diverse reasoning styles across the dataset.

This differs from priority-based deduplication where one model would "win" all duplicates. With fair distribution, each model gets roughly equal share of the shared question pool.

Schema

Each sample follows the conversational messages format with reasoning content:

{
  "messages": [
    {
      "role": "system",
      "content": "You are a medical expert...",
      "reasoning_content": null
    },
    {
      "role": "user",
      "content": "What are the symptoms of diabetes?",
      "reasoning_content": null
    },
    {
      "role": "assistant",
      "content": "The main symptoms of diabetes include...",
      "reasoning_content": "Let me think through this systematically..."
    }
  ]
}

Fields

Field	Type	Description
messages	list	Array of message objects in the conversation
messages[].role	string	Either "system", "user", or "assistant"
messages[].content	string	The main message content
messages[].reasoning_content	string or null	Chain-of-thought reasoning (assistant messages only)

Usage

Loading with Datasets Library

from datasets import load_dataset

dataset = load_dataset("OpenMed/Medical-Reasoning-SFT-Mega")

Accessing Samples

# Get a sample
sample = dataset['train'][0]

# Access messages
for msg in sample['messages']:
    print(f"Role: {msg['role']}")
    print(f"Content: {msg['content'][:100]}...")
    if msg['reasoning_content']:
        print(f"Reasoning: {msg['reasoning_content'][:100]}...")

Filtering by Reasoning

# Get samples with reasoning content
samples_with_reasoning = dataset['train'].filter(
    lambda x: x['messages'][-1]['reasoning_content'] is not None
)

Intended Use

This dataset is designed for:

• Fine-tuning medical reasoning models: Train LLMs with diverse reasoning styles from multiple state-of-the-art models
• Chain-of-thought training: Develop models that show detailed thinking processes
• Medical QA systems: Build robust question-answering systems for healthcare applications
• Research: Study and compare reasoning patterns across different AI architectures
• Distillation: Transfer capabilities from multiple large models to smaller ones

Why Mega?

1. Diversity: 7 different model architectures provide varied reasoning approaches
2. Fair Representation: Fair distribution ensures balanced contributions from all models
3. Scale: 1.79M unique samples and 3.78B tokens for comprehensive training
4. Coverage: Spans clinical, diagnostic, pharmacological, and general medical knowledge

Limitations and Considerations

• This dataset is generated by AI models and should not be used as a substitute for professional medical advice
• Responses may contain inaccuracies and should be validated by medical professionals
• Not intended for clinical decision-making without expert review
• The reasoning traces reflect model approaches, not necessarily optimal clinical reasoning

Related Datasets

Individual model datasets are also available:

• Medical-Reasoning-SFT-MiniMax-M2.1
• Medical-Reasoning-SFT-Baichuan-M3-235B
• Medical-Reasoning-SFT-GPT-OSS-120B-V2
• Medical-Reasoning-SFT-Qwen3-Next-80B
• Medical-Reasoning-SFT-GLM_4.5_Air
• Medical-Reasoning-SFT-Nemotron-Nano-30B
• Medical-Reasoning-SFT-Trinity-Mini

Citation

If you use this dataset, please cite:

@dataset{medical_reasoning_sft_mega,
  title={Medical-Reasoning-SFT-Mega},
  author={OpenMed},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Mega}
}

License

Apache 2.0