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README.md

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-license: mit
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+---
+license: mit
+---
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+# Difficulty Estimation on DeepScaleR
+
+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.
+
+## 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:
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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)
+
+## 📬 Contact
+
+For questions or feedback, feel free to reach out to **Taiwei Shi** at [taiweish@usc.edu](mailto:taiweish@usc.edu).