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---
license: mit
configs:
- config_name: Difficulty Score
data_files: Qwen2.5-Math-7B--orz--difficulty.csv
- config_name: Response
data_files: Qwen2.5-Math-7B--orz.csv
---
## Difficulty Estimation on Open Reasoner Zero
We annotate the entire [**Open Reasoner Zero**]((https://huggingface.co/Open-Reasoner-Zero/Open-Reasoner-Zero-7B)) dataset with a **difficulty score** based on the performance of the [Qwen 2.5-MATH-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) model. This provides an adaptive signal for curriculum construction.
Open Reasoner Zero is a curated a dataset of 57,000 reasoning-intensive problems used to train and evaluate reinforcement learning-based methods for large language models.
## Difficulty Scoring Method
Difficulty scores are estimated using the **Qwen 2.5-MATH-7B** model with the following generation settings:
- `temperature = 0.6`
- `top_p = 0.9`
- `max_tokens = 4096`
- Inference performed using [vLLM](https://github.com/vllm-project/vllm)
- Each problem is attempted **128 times**
The difficulty score `d_i` for each problem is computed as:
d_i = 100 × (1 - (# successes / 128))
This approach balances the evaluation signal:
- A **strong model** would trivially solve easy problems, compressing the difficulty scale.
- A **weak model** would fail uniformly, providing poor resolution.
- Qwen 2.5-MATH-7B was selected for its **mid-range capabilities**, offering meaningful gradients across a wide spectrum of problems.
## Difficulty Estimation on Other Datasets
We also apply the same difficulty estimation procedure to the following datasets:
- [Open Reasoner Zero](https://huggingface.co/datasets/lime-nlp/orz_math_difficulty)
- [DeepScaleR](https://huggingface.co/datasets/lime-nlp/DeepScaleR_Difficulty)
- [MATH](https://huggingface.co/datasets/lime-nlp/MATH_difficulty)
- [GSM8K](https://huggingface.co/datasets/lime-nlp/GSM8K_difficulty)
## 📬 Contact
For questions or feedback, feel free to reach out to [**Taiwei Shi**](https://maksimstw.github.io/) at [[email protected]](mailto:[email protected]).
## 📚 Citations
Github: https://github.com/uscnlp-lime/verl
If you find our dataset useful, please cite [Efficient Reinforcement Finetuning via Adaptive Curriculum Learning](https://huggingface.co/papers/2504.05520):
```bibtex
@misc{shi2025efficientreinforcementfinetuningadaptive,
title={Efficient Reinforcement Finetuning via Adaptive Curriculum Learning},
author={Taiwei Shi and Yiyang Wu and Linxin Song and Tianyi Zhou and Jieyu Zhao},
year={2025},
eprint={2504.05520},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.05520},
}
``` |