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README.md

@@ -1,55 +1,79 @@
 ---
-dataset_info:
-  features:
-  - name: data_source
-    dtype: string
-  - name: prompt
-    list:
-    - name: content
-      dtype: string
-    - name: role
-      dtype: string
-  - name: ability
-    dtype: string
-  - name: reward_model
-    struct:
-    - name: ground_truth
-      struct:
-      - name: numbers
-        list: int64
-      - name: target
-        dtype: float64
-    - name: style
-      dtype: string
-  - name: extra_info
-    struct:
-    - name: index
-      dtype: int64
-    - name: numbers
-      list: int64
-    - name: solution
-      dtype: string
-    - name: target
-      dtype: float64
-  splits:
-  - name: train
-    num_bytes: 132408
-    num_examples: 200
-  - name: validation
-    num_bytes: 1191445
-    num_examples: 1800
-  - name: test
-    num_bytes: 132597
-    num_examples: 200
-  download_size: 1395212
-  dataset_size: 1456450
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+language:
+- en
+license: mit
+task_categories:
+- text-generation
+- question-answering
+tags:
+- math
+- reasoning
+- gsm8k
+- countdown
+- evolution-strategies
+- reinforcement-learning
+size_categories:
+- 1K<n<10K
 ---
+
+# COUNTDOWN Dataset for ES vs GRPO Comparison
+
+## Dataset Description
+
+This dataset contains prepared splits of the COUNTDOWN task for comparing Evolution Strategies (ES) and Group Relative Policy Optimization (GRPO) methods for LLM fine-tuning.
+
+### Dataset Statistics
+
+- **Training split:** 10.0% of available data
+- **Training samples:** 200
+- **Validation samples:** 1,800
+- **Test samples:** 200 (reserved for final evaluation)
+
+### Data Format
+
+Each example contains:
+- `data`: The input prompt/question
+- `answer`: Ground truth answer
+- Additional task-specific fields
+
+### Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("your-username/countdown-es-grpo")
+
+# Access splits
+train_data = dataset["train"]
+val_data = dataset["validation"]
+test_data = dataset["test"]
+
+# Example
+print(train_data[0])
+```
+
+### Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@misc{qiu2025evolutionstrategiesscalellm,
+      title={Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning}, 
+      author={Xin Qiu and Yulu Gan and Conor F. Hayes and Qiyao Liang and Elliot Meyerson and Babak Hodjat and Risto Miikkulainen},
+      year={2025},
+      eprint={2509.24372},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2509.24372}, 
+}
+```
+
+### Source
+
+- **Repository:** https://github.com/alphaXiv/paper-implementations
+- **Paper:** https://arxiv.org/abs/2509.24372
+
+### License
+
+MIT License