Medical Reasoning Dataset For Supervised Fine-Tuning

OpenMedicalReasoning contains approximately 19,700 samples designed for supervised fine-tuning in medical reasoning tasks. OrionLLM created this dataset, which was last updated on March 15, 2026.

Use Cases

• Fine-tune a language model on medical reasoning tasks using the dataset's ~19.7k text samples.
• Train models to perform clinical text analysis based on the reasoning patterns present in the data.
• Benchmark model performance on medical SFT tasks using the provided collection of samples.

Strengths

• ~19,700 samples provides a substantial corpus for model training.
• Dataset is specifically designed for the supervised fine-tuning task.

Limitations

• Sample size is moderate for modern LLM fine-tuning.
• Specific column names and data structure are unknown.
• Potential for bias or gaps in medical coverage is unverified.

Provenance

Source

OrionLLM

Collection Method

Collected from an unspecified source (o1).

Freshness

Last updated March 15, 2026.