metadata

license: mit
configs:
  - config_name: Difficulty Score
    data_files: Qwen2.5-Math-7B--deepscaler--difficulty.csv
  - config_name: Response
    data_files: Qwen2.5-Math-7B--deepscaler.csv
task_categories:
  - reinforcement-learning

Difficulty Estimation on DeepScaleR

We annotate the entire DeepScaleR 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 and model evaluation.

DeepScaleR is a curated dataset of 40,000 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
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:

📬 Contact

For questions or feedback, feel free to reach out to Taiwei Shi at [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:

@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}, 
}