---
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.