Datasets:

lime-nlp
/

orz_math_difficulty

We annotate the entire Open Reasoner Zero dataset with a difficulty score based on the performance of the Qwen 2.5-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 via vLLM
Each problem is attempted 128 times

The difficulty score for each problem is computed as:

 d_i = 100 × (1 - (# successes / 128))

This scoring approach ensures a balanced estimation: a strong model would trivially succeed on all problems, undermining difficulty measurement, while a weak model would fail uniformly, limiting the usefulness of the signal. Qwen 2.5-MATH-7B was chosen for its mid-range capabilities, providing informative gradients in problem difficulty across the dataset.

Contact

Feel free to contact Taiwei Shi (taiweish@usc.edu) if you have any questions.