Tralalabs/reasoning-prompts

🧠 Reasoning Prompts — Tralalabs

A focused collection of reasoning-oriented prompts designed to push language models to think step-by-step, reason through complex scenarios, and produce structured, logical outputs.

Built and maintained by Tralalabs 🚀

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📦 Dataset Summary

Field	Value
Namespace	Tralalabs/reasoning-prompts
Format	Plain text (single column)
Column	text
Language	English
License	Apache 2.0

Each row contains a single text field holding a reasoning prompt — no labels, no metadata, just raw prompts ready to be used for training, fine-tuning, or evaluation.

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🗂️ Dataset Structure

DatasetDict({
    train: Dataset({
        features: {
            'text': Value(dtype='string')
        }
    })
})

Example Row

{
  "text": "If a train leaves at 9:00 AM traveling at 80 km/h and another leaves at 10:00 AM at 120 km/h in the same direction, when does the second train catch up?"
}

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🚀 Usage

Load with 🤗 Datasets

from datasets import load_dataset

ds = load_dataset("Tralalabs/reasoning-prompts")
print(ds["train"][0]["text"])

Use as Pretraining Data

from datasets import load_dataset

ds = load_dataset("Tralalabs/reasoning-prompts", split="train")
texts = [row["text"] for row in ds]

Fine-tuning with a System Prompt

from datasets import load_dataset

ds = load_dataset("Tralalabs/reasoning-prompts", split="train")

def format_prompt(row):
    return {
        "text": f"<|system|>\nYou are a reasoning assistant. Think step by step.\n<|user|>\n{row['text']}\n<|assistant|>\n"
    }

ds = ds.map(format_prompt)

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🎯 Intended Uses

• Pretraining small language models with reasoning-heavy signal
• Fine-tuning instruction-following models (LoRA, QLoRA, full fine-tune)
• Evaluation of chain-of-thought and step-by-step reasoning capabilities
• Prompt engineering research and benchmarking

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⚠️ Out-of-Scope Uses

• Not intended for safety-critical decision making
• Not a benchmark dataset — no gold-standard answers included
• Not suitable for tasks requiring factual grounding without additional context

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🛠️ Dataset Creation

Prompts were collected and curated to specifically target logical reasoning, mathematical thinking, causal inference, and multi-step problem solving — the kinds of tasks that separate capable models from autocomplete machines 💀

Sourced from a .txt file (Reasoning-Prompts.txt) and converted to HuggingFace-compatible Parquet format.

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📜 License

This dataset is released under the Apache 2.0 license. Free to use for research and commercial purposes with attribution.

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🏷️ Citation

@dataset{tralalabs_reasoning_prompts,
  author    = {Tralalabs},
  title     = {Reasoning Prompts},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/Tralalabs/reasoning-prompts}
}

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🤝 About Tralalabs

Tralalabs builds open datasets, small language models, and fine-tuned checkpoints. Find more of our work at huggingface.co/Tralalabs 🔥