Kwai-Klear/KlearReasoner-CodeSub-15K

Dataset Summary

This dataset is a high-quality subset of the Klear-Reasoner Code RL dataset, derived from the RL data used in the rllm project. Part of this data contributed to training Klear-Reasoner’s code reasoning models. The dataset is carefully cleaned and filtered to include only reliable samples suitable for reinforcement learning. Models trained with this dataset have shown substantial performance improvements across various code reasoning benchmarks. You can load the dataset via the Hugging Face datasets library:

from datasets import load_dataset
dataset = load_dataset("Kwai-Klear/KlearReasoner-CodeSub-15K")

Resource	Link
📝 Preprints	Paper
🤗 Daily Paper	Paper
🤗 Model Hub	Klear-Reasoner-8B
🤗 Dataset Hub	Math RL
🤗 Dataset Hub	Code RL
🐛 Issues & Discussions	GitHub Issues
📧 Contact	[email protected]

Data Fields

• data_source (string) — The source identifier for the sample.
• prompt (list of dict) — The input prompt, stored as a list of message objects in chat format.
• ability (string) — The skill or task category associated with the sample.
• reward_model (dict) — Information about the ground truth or reward signal.
  • ground_truth (string) — The expected correct answer (may include LaTeX formatting).
  • style (string) — The method or type of evaluation, e.g., "rule".
• index_level_0 (int) — An internal index or unique identifier for the sample.

Demonstration of Data Quality

This dataset contains exclusively high-quality, filtered samples.
All samples have been selected to ensure accurate reward signals for reinforcement learning, following the gradient-preserving clipping policy optimization (GPPO) method introduced in our paper. Models trained using this dataset achieve strong generalization and reliable performance on a range of math reasoning tasks.

Citation

If you find this work helpful, please cite our paper:

@misc{su2025cegppocontrollingentropygradientpreserving,
      title={CE-GPPO: Controlling Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning}, 
      author={Zhenpeng Su and Leiyu Pan and Minxuan Lv and Yuntao Li and Wenping Hu and Fuzheng Zhang and Kun Gai and Guorui Zhou},
      year={2025},
      eprint={2509.20712},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2509.20712}, 
}

@article{DBLP:journals/corr/abs-2508-07629,
  author       = {Zhenpeng Su and
                  Leiyu Pan and
                  Xue Bai and
                  Dening Liu and
                  Guanting Dong and
                  Jiaming Huang and
                  Wenping Hu and
                  Fuzheng Zhang and
                  Kun Gai and
                  Guorui Zhou},
  title        = {Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving
                  Clipping Policy Optimization},
  journal      = {CoRR},
  volume       = {abs/2508.07629},
  year         = {2025},
  url          = {https://doi.org/10.48550/arXiv.2508.07629},
  doi          = {10.48550/ARXIV.2508.07629},
  eprinttype    = {arXiv},
  eprint       = {2508.07629},
  timestamp    = {Sat, 13 Sep 2025 14:46:27 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2508-07629.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}