Update README.md

README.md

@@ -6,21 +6,33 @@ license: mit
 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 Scoring Method
 
-Difficulty scores are estimated using the Qwen 2.5-MATH-7B model with the following generation settings:
+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](https://github.com/vllm-project/vllm)
+- `max_tokens = 4096`
+- Inference performed using [vLLM](https://github.com/vllm-project/vllm)
 - Each problem is attempted **128 times**
 
-The difficulty score for each problem is computed as:
+The difficulty score `d_i` 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.
+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.
 
-## Contact
-Feel free to contact Taiwei Shi ([email protected]) if you have any questions.
+## Difficulty Estimation on Other Datasets
+
+We also apply the same difficulty estimation procedure to the following datasets:
+
+- [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**](https://maksimstw.github.io/) at [[email protected]](mailto:[email protected]).