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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 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:
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+- `temperature = 0.6`
+- `top_p = 0.9`
+- Inference performed via [vLLM](https://github.com/vllm-project/vllm)
+- Each problem is attempted **128 times**
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+The difficulty score for each problem is computed as:
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+     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.