| --- |
| dataset_info: |
| features: |
| - name: fen |
| dtype: string |
| - name: rating |
| dtype: int64 |
| - name: tags |
| list: string |
| - name: turn |
| dtype: string |
| - name: uci_moves |
| list: string |
| - name: uuid |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 867658667 |
| num_examples: 4278346 |
| - name: validation |
| num_bytes: 102119282 |
| num_examples: 503581 |
| - name: test |
| num_bytes: 51001348 |
| num_examples: 251434 |
| download_size: 786486627 |
| dataset_size: 1020779297 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: validation |
| path: data/validation-* |
| - split: test |
| path: data/test-* |
| license: mit |
| language: |
| - en |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
|
|
| # Dataset Card: Chess-Puzzles-RLVR |
|
|
| ## Dataset Summary |
| This dataset is a highly processed and stratified collection of approximately **5 million chess puzzles**, ranging from Elo ratings of **400 to 3300**. It is specifically designed for **Curriculum Learning** and **Reinforcement Learning (RLVR)** agents. |
|
|
| Unlike standard puzzle datasets, this version is pre-sorted and split into "rating buckets" to ensure that training, validation, and testing sets maintain an identical difficulty distribution across the entire spectrum. |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
| Each instance represents a unique chess puzzle with a starting position (FEN) and the correct sequence of moves (UCI). |
|
|
| ### Key Fields (Schema) |
| * **`fen`** *(string)*: The Forsyth-Edwards Notation representing the board state before the first move of the puzzle. |
| * **`uci_moves`** *(list of strings)*: The sequence of best moves in Universal Chess Interface (UCI) format (e.g., `["e2e4", "e7e5"]`). |
| * **`rating`** *(int)*: The difficulty rating of the puzzle (Elo). |
| * **`tags`** *(list of strings)*: Tactical motifs associated with the puzzle (e.g., `["fork", "sacrifice", "mateIn2"]`). |
| * **`turn`** *(string)*: Indicates which side is to move in the starting FEN (`"White"` or `"Black"`). |
| |
| ### Data Splits |
| The dataset is split into three parts, with each split containing a proportionate amount of data from every 100-point rating interval: |
| |
| | Split | Percentage | Purpose | |
| | :--- | :--- | :--- | |
| | **Train** | 85% | Primary data for model training. | |
| | **Validation** | 10% | Monitoring performance and preventing catastrophic forgetting across difficulty tiers. | |
| | **Test** | 5% | Final holdout set for objective evaluation. | |
| |
| --- |
| |
| ## Creation Process |
| |
| ### 1. Data Cleaning and Transformation |
| The dataset was transformed from a raw chess puzzle format using the `python-chess` library. The following steps were taken for every row: |
| |
| * **Turn Extraction**: The active color was parsed directly from the FEN string. |
| * **String Tokenization**: Raw space-separated strings for `moves` and `tags` were converted into clean Python lists for easier model consumption. |
| * **Feature Pruning**: Redundant boolean flags (e.g., `white_kingside`, `board`) were removed to reduce the dataset footprint and focus strictly on necessary state representation. |
| |
| ### 2. Stratified Bucketing |
| To facilitate curriculum learning, the dataset underwent a unique **Stratified Bucketing** process: |
|
|
| 1. The entire dataset was sorted globally by **rating**. |
| 2. The data was partitioned into **29 buckets**, each representing a 100-point rating range (e.g., 400-500, 501-600, ..., 3200-3300). |
| 3. The 85/10/5 split was applied **locally within each bucket**. |
| 4. These local splits were then re-concatenated into the final global `train`, `validation`, and `test` splits. |
|
|
| This ensures that whether the model is training on "easy" or "hard" data, the validation set always provides a statistically accurate reflection of the model's ability across the entire difficulty spectrum. |
|
|
| --- |
|
|
| ## Usage Considerations |
| This dataset is optimized for a **Sliding Window Sampler**. During training, it is recommended to: |
|
|
| 1. Sample **80%** of your batch from the model's current "target" rating bucket. |
| 2. Sample **20%** from all previously learned (easier) buckets to maintain tactical proficiency and prevent regression. |
