alucent/mirror-GLM-5.1-Reasoning-1M-Cleaned

GLM-5.1-Reasoning-1M-Cleaned

GLM-5.1-Reasoning-1M-Cleaned is a cleaned and reformatted derivative of Kassadin88/GLM-5.1-1000000x. It preserves the original four-subset layout (main, PHD-Science, Multilingual-STEM, Math) while converting every example into a unified SFT-ready schema with explicit conversations, input, output, domain, and meta fields.

This release was prepared from the original dataset published by Kassadin88.

Summary

• Teacher model in the data: GLM-5.1
• Total processed records: 766,535
• Total kept records: 746,321
• Total removed records: 20,214
• Subset layout preserved exactly as the source dataset: main, PHD-Science, Multilingual-STEM, Math

Included Content

• main: general reasoning and instruction-following data.
• PHD-Science: graduate-level physics, chemistry, and biology reasoning traces.
• Multilingual-STEM: multilingual STEM reasoning data, including Chinese, English, and other languages present in the source release.
• Math: mathematics-heavy reasoning and proof-style responses.

Cleaning and Reformatting

The raw source dataset mixed two answer layouts:

1. Standard <think>...</think> reasoning tags.
2. A non-standard short-dash wrapper around the reasoning section.

This cleaned release normalizes both styles into a single output format:

{
  "id": "md5-hash-of-domain-input-reasoning-answer",
  "conversations": [
    {"from": "human", "value": "user prompt"},
    {"from": "gpt", "value": "<think>\nreasoning trace\n</think>\n\nfinal answer"}
  ],
  "input": "user prompt",
  "output": "<think>\nreasoning trace\n</think>\n\nfinal answer",
  "domain": "subset/domain name from the original _id prefix",
  "meta": {
    "input_tokens": 123,
    "output_tokens": 456,
    "teacher_model": "GLM-5.1"
  }
}

Removed data

The cleaning pipeline removed records with:

• incomplete or obviously truncated answers,
• repeated reasoning paragraphs or duplicated answer segments,
• refusal-style answers,
• unparseable reasoning/answer boundaries,
• exact duplicate records after normalization.

Subset Statistics

Subset	Processed	Kept	Removed	File Size	Median Input Tokens	Median Output Tokens
main	547,292	527,737	19,555	18.28 GB	50	3008
PHD-Science	103,759	103,706	53	3.72 GB	45	3387
Multilingual-STEM	93,032	92,781	251	3.53 GB	66	4084
Math	22,452	22,097	355	4.03 GB	59	24498

Filter Statistics

Subset	Issue	Removed
main	incomplete_output	12,726
main	repeated_paragraph	6,152
main	refusal_answer	638
main	unparseable_output	38
main	duplicate_record	1
PHD-Science	incomplete_output	33
PHD-Science	repeated_paragraph	19
PHD-Science	refusal_answer	1
Multilingual-STEM	repeated_paragraph	116
Multilingual-STEM	incomplete_output	88
Multilingual-STEM	refusal_answer	28
Multilingual-STEM	unparseable_output	19
Math	repeated_paragraph	303
Math	incomplete_output	26
Math	unparseable_output	26

Additional Token Statistics

Subset	Mean Input Tokens	P95 Input Tokens	Mean Output Tokens	P95 Output Tokens
main	118.62	515	4482.35	15041
PHD-Science	45.05	56	4387.73	10447
Multilingual-STEM	79.91	192	5461.45	11034
Math	62.01	89	28133.4	64633

Data Structure

Each example is a single-turn reasoning distillation sample:

• conversations[0]: the user prompt.
• conversations[1]: the model response with reasoning wrapped in <think>...</think> and the final answer after the closing tag.
• input: prompt-only view for training pipelines that prefer flat prompt fields.
• output: tagged answer-only view for training pipelines that prefer flat completion fields.
• domain: original subset/domain name extracted from the source record ID.
• meta: lightweight per-example metadata.

Usage

from datasets import load_dataset

main = load_dataset("Jackrong/GLM5.1-Reasoning-1M-Cleaned", "main")
science = load_dataset("Jackrong/GLM5.1-Reasoning-1M-Cleaned", "PHD-Science")
stem = load_dataset("Jackrong/GLM5.1-Reasoning-1M-Cleaned", "Multilingual-STEM")
math = load_dataset("Jackrong/GLM5.1-Reasoning-1M-Cleaned", "Math")

If you publish under a different namespace, replace Jackrong/ with your actual Hugging Face username or org.

Provenance

This dataset is derived from:

• Original dataset: Kassadin88/GLM-5.1-1000000x
• Original author: Kassadin88

Citation

Please cite the original dataset first:

@misc{glm51-1000000x,
  title={GLM-5.1-1000000x: One Million Reasoning Traces Distilled from GLM-5.1},
  author={Kassadin88},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/Kassadin88/GLM-5.1-1000000x}
}

You can additionally cite this cleaned derivative release as:

@misc{glm51_reasoning_1m_cleaned,
  title={GLM-5.1-Reasoning-1M-Cleaned},
  author={Jackrong},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/Jackrong/GLM5.1-Reasoning-1M-Cleaned}
}