---
license: other
language:
- en
pretty_name: GCC 2025 chess policy datasets (searchless + gold)
tags:
- chess
- distillation
- policy
- searchless
- stockfish
- uci
- global-chess-challenge-2025
---

# GCC 2025 Chess Policy Datasets (searchless + gold)

This repo contains the datasets used to train and evaluate a text-only chess policy model for the Global Chess Challenge 2025.

## Files
- `searchless.jsonl`: 2,048,000 searchless teacher-labeled positions.
- `gold.jsonl`: 208,832 Stockfish-labeled positions (gold).
- `labels_merged.jsonl`: merged labels (searchless + gold).
- `policy_sft_v2.jsonl`: SFT training set with `gold_repeat=3` applied.
- `labels_eval_5000.jsonl`: fixed 5k canary set for offline evaluation.
- `notes/data_assets_20251231T220044Z.md`: dataset inventory and backup notes.

## Schema
### Common fields
- `fen`: FEN string
- `move_history_uci`: optional move history (UCI)
- `legal_moves_uci`: list of legal moves (UCI)
- `best_move`: labeled best move (UCI)
- `label_source`: `searchless` or `stockfish`

### Searchless-only fields
- `teacher_model`, `teacher_checkpoint_step`
- `top_moves`, `top_win_probs`
- `bottom_moves`, `bottom_win_probs`
- `best_win_prob`

### Gold-only fields
- `stockfish_depth`

## Intended use
- Supervised policy SFT for chess move selection.
- Preference/ranking training using top/bottom move lists and win probabilities.

## Notes
- Prompt templates are stored in the training repo; this dataset only includes labels.
- The canary set is fixed to enable paired comparisons across checkpoints.