Jackrong/LogicMind-Chat-Reasoning-SFT-300K

Nemotron-Post-Training-Dataset-v2-chat Dataset Card

Overview 📌

This dataset contains 296,168 chat-style instruction/response samples generated by qwen-3-32b. Each record provides a user prompt, an explicit reasoning trace, and a final answer, plus precomputed length fields. The data is packaged as JSONL (one JSON object per line).

Highlights

• Scale: 296,168 samples
• Category: chat (100%)
• Generator: qwen-3-32b (100%)
• Structure: problem → qwen3-reasoning → qwen3-solution with length metadata
• Coverage: Multi-domain instructions and multilingual content (based on random sampling)

Dataset Structure 🧩

Fields

Field	Type	Description
id	string	Unique record identifier
generator	string	Model tag for data generation (qwen-3-32b)
category	string	Data category (chat)
problem	string	User prompt or instruction
qwen3-reasoning	string	Chain-of-thought style reasoning trace
qwen3-solution	string	Final answer to the prompt
len_qwen3_reasoning	int	Length of reasoning text (provided)
len_qwen3_solution	int	Length of final answer text (provided)

Example (schema)

{
  "id": "...",
  "generator": "qwen-3-32b",
  "category": "chat",
  "problem": "...",
  "qwen3-reasoning": "...",
  "qwen3-solution": "...",
  "len_qwen3_reasoning": 123,
  "len_qwen3_solution": 456
}

Statistical Summary 📊

Total samples: 296,168

Length statistics (characters / length values)

Field	Median	Mean	P95	Max
problem	207	724.72	3,488	56,088
qwen3-reasoning	701	914.63	2,499	8,333
qwen3-solution	761	768.62	1,526	9,700

Answer-to-reasoning length ratio (only for len_qwen3_reasoning > 0)

• Median: 0.93
• P95: 2.49

Visualizations 🧪

• Reasoning vs. solution lengths:

Intended Uses 💡

• Instruction tuning for chat and multi-domain assistants
• Chain-of-thought distillation or reasoning supervision
• Complex QA and multi-step problem solving
• Benchmarking reasoning length control and response compression

Data Characteristics 🔎

• The dataset is fully synthetic (model-generated), not human-authored.
• The reasoning traces are explicit and may include sensitive or unnecessary intermediate steps for deployment in certain applications.
• The distribution of languages and domains is non-uniform; for downstream tasks, consider stratified sampling or domain filtering.

Limitations ⚠️

• Hallucination risk: model-generated data may contain factual errors.
• Reasoning leakage: the presence of explicit reasoning may be undesirable for certain production use cases.
• Domain bias: real-world coverage is not guaranteed across all subject areas.

Data Splits 🧩

• No official train/validation/test split is provided. Users should create their own splits as needed.

License & Usage 👀

• Please follow the license terms associated with the source repository or data release.
• If you use this dataset, cite the original source and acknowledge the generation model.

Citation 🙏

@misc{nvidia2025nemotronv2,
  title        = {Nemotron-Post-Training-Dataset-v2},
  author       = {NVIDIA},
  year         = {2025},
  note         = {Includes SFT and RL data across math, code, reasoning, and chat tasks},
}

@misc{Jackrong2026dataset,
  title        = {LogicMind-Chat-Reasoning-SFT-300K},
  author       = {Jackrong},
  year         = {2026},
  note         = {A dataset constructed using seed questions inspired by NVIDIA's Nemotron-Post-Training-Dataset-v2-chat}
}