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| license: mit |
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| # Difficulty Estimation on DeepScaleR |
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| We annotate the entire [**DeepScaleR**](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview) 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 and model evaluation. |
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| **DeepScaleR** is a curated dataset of 40,000 reasoning-intensive problems used to train and evaluate reinforcement learning-based methods for large language models. |
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| ## Difficulty Scoring Method |
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| Difficulty scores are estimated using the **Qwen 2.5-MATH-7B** model with the following generation settings: |
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| - `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** |
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| The difficulty score `d_i` for each problem is computed as: |
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| d_i = 100 × (1 - (# successes / 128)) |
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| 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. |
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| ## Difficulty Estimation on Other Datasets |
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| We also apply the same difficulty estimation procedure to the following datasets: |
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| - [Open Reasoner Zero](https://huggingface.co/datasets/lime-nlp/orz_math_difficulty) |
| - [MATH](https://huggingface.co/datasets/lime-nlp/MATH_difficulty) |
| - [GSM8K](https://huggingface.co/datasets/lime-nlp/GSM8K_difficulty) |
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| ## 📬 Contact |
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| For questions or feedback, feel free to reach out to [**Taiwei Shi**](https://maksimstw.github.io/) at [taiweish@usc.edu](mailto:taiweish@usc.edu). |
