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 ## One-shot Entropy Minimization
 
-<a href='https://arxiv.org/abs/2505.20282'><img src='https://img.shields.io/badge/arXiv-2505.20282-b31b1b.svg'></a> &nbsp;
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+<a href='https://arxiv.org/abs/2505.20282'><img src='https://img.shields.io/badge/arXiv-2505.20282-b31b1b.svg'></a> &nbsp;
+
+### Overview
+
+This repository provides code and instructions to reproduce the results of the **One-shot Entropy Minimization (EM)** method presented in our paper. It includes both 1-shot and multi-shot training setups, as well as evaluation using the Qwen2.5-Math benchmark.
+
+---
+
+### Reproducing One-shot EM Training (SOTA)
+
+```bash
+accelerate launch train.py --lr 2e-5 --temperature 0.5 --bsz 64
+```
+
+---
+
+### Reproducing Multi-shot EM Training
+
+```bash
+accelerate launch train.py --lr 2e-5 --temperature 0.5 --bsz 64 --data_path "dataset/numina/numina_00.parquet"
+```
+
+---
+
+### Evaluation
+
+```bash
+cd Qwen2.5-Eval/evaluation
+bash sh/eval_all_math.sh
+```
+
+---
+
+### Acknowledgements
+
+Our dataset references and builds upon the following open-source contributions:
+
+- [NuminaMath-CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT)
+- [DeepScaler](https://github.com/agentica-project/deepscaler)
+- [One-shot RLVR](https://github.com/ypwang61/One-Shot-RLVR/) – for data selection strategies
+- [Qwen2.5-Eval](https://github.com/QwenLM/Qwen2.5-Math/) – for evaluation benchmarks
+
+We sincerely thank the authors and maintainers of these projects for their excellent contributions to the research community!
+
+
+---
+
+### Citation
+```
+@misc{gao2025oneshotentropyminimization,
+      title={One-shot Entropy Minimization}, 
+      author={Zitian Gao and Lynx Chen and Joey Zhou and Bryan Dai},
+      year={2025},
+      eprint={2505.20282},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.20282}, 
+}
+```
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