SmallDoge/SmallThoughts

Dataset

SmallThoughts

47

20 commits

3 linked in READMEs

updated Jul 24, 2025

See the code

README

SmallThoughts

Small-Thoughts Map

Open synthetic reasoning dataset, covering math, science, code, and puzzles.

To address the issue of the existing DeepSeek R1 distilled data being too long, this dataset constrains the reasoning trajectory to be more precise and concise while retaining the reflective nature.

We also open-sourced the pipeline code for distilled data here, with just one command you can generate your own dataset.

How to use

You can load the dataset with the following code:

import datasets
dataset = datasets.load_dataset("SmallDoge/SmallThoughts")

If you are using TRL for model training, The problem and solution columns can be used for GRPO reinforcement learning, and the messages columns can be used for SFT fine-tuning, without any additional preprocessing.

Visualization

All examples, clustered by semantic similarity, can be explored in Nomic Atlas.

Nomic Atlas Small-Thoughts Map

License

This dataset is released under the Apache-2.0 License.

Citation

@misc{wu2025concisereasoningbiggains,
      title={Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with Difficulty-Aware Prompting}, 
      author={Yifan Wu and Jingze Shi and Bingheng Wu and Jiayi Zhang and Xiaotian Lin and Nan Tang and Yuyu Luo},
      year={2025},
      eprint={2505.19716},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2505.19716}, 
}
biology
chemistry
code
synthetic

Contributors

JingzeShi

19 commits

wubingheng

1 commits

SmallDoge/SmallThoughts

Dataset

SmallThoughts

47

20 commits

3 linked in READMEs

updated Jul 24, 2025

See the code

README

SmallThoughts

Small-Thoughts Map

Open synthetic reasoning dataset, covering math, science, code, and puzzles.

To address the issue of the existing DeepSeek R1 distilled data being too long, this dataset constrains the reasoning trajectory to be more precise and concise while retaining the reflective nature.

We also open-sourced the pipeline code for distilled data here, with just one command you can generate your own dataset.

How to use

You can load the dataset with the following code:

import datasets
dataset = datasets.load_dataset("SmallDoge/SmallThoughts")

If you are using TRL for model training, The problem and solution columns can be used for GRPO reinforcement learning, and the messages columns can be used for SFT fine-tuning, without any additional preprocessing.

Visualization

All examples, clustered by semantic similarity, can be explored in Nomic Atlas.

Nomic Atlas Small-Thoughts Map

License

This dataset is released under the Apache-2.0 License.

Citation

@misc{wu2025concisereasoningbiggains,
      title={Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with Difficulty-Aware Prompting}, 
      author={Yifan Wu and Jingze Shi and Bingheng Wu and Jiayi Zhang and Xiaotian Lin and Nan Tang and Yuyu Luo},
      year={2025},
      eprint={2505.19716},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2505.19716}, 
}
biology
chemistry
code
synthetic

Contributors

JingzeShi

19 commits

wubingheng

1 commits