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metadata
license: mit
task_categories:
  - other
tags:
  - chess
  - stockfish
  - self-play
  - pawn
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
      - split: validation
        path: data/validation-00000-of-00001.parquet
      - split: test
        path: data/test-00000-of-00001.parquet

Stockfish Self-Play (nodes=1)

1M games of Stockfish 17 self-play at 1 node per move. Pre-tokenized in the PAWN training format.

At nodes=1, Stockfish evaluates each position with a single NNUE forward pass (no tree search). Despite the lack of search, the NNUE evaluation head produces surprisingly strong play — far from random. Games exhibit coherent openings, reasonable piece development, and tactical awareness, though blunders are more frequent than at higher node counts. This makes the dataset a useful intermediate between fully random games and strong engine play.

Schema

Column Type Description
tokens list[int16] PAWN token IDs per ply (variable length, max 255)
game_length uint16 Number of half-moves
result string Game result (1-0, 0-1, 1/2-1/2, *)

Token vocabulary: 4,278 tokens (1 PAD + 4,096 grid moves + 176 promotions + 5 outcomes). See the PAWN architecture docs for details.

Usage

from datasets import load_dataset

ds = load_dataset("thomas-schweich/stockfish-nodes1")
game = ds["train"][0]
print(game["tokens"])       # [919, 3300, 659, ...]
print(game["result"])       # "0-1"
print(game["game_length"])  # 74

Or with Polars:

import polars as pl

df = pl.scan_parquet("hf://datasets/thomas-schweich/stockfish-nodes1/data/*.parquet")
print(df.head(5).collect())

Generation

Games were generated with Stockfish 17 using the Rust UCI engine interface in the PAWN repository (engine/src/engine_gen.rs).

Parameters:

  • Nodes per move: 1 (single NNUE evaluation, no search)
  • Opening diversity: MultiPV=5 with softmax temperature sampling throughout the full game
  • Temperature: 1.0 (1 pawn difference ≈ e-fold probability ratio)
  • Max ply: 500 (games exceeding this are drawn)
  • Workers: 16 parallel engines, deterministic seeds (10000–10015)
  • Format: zstd-compressed Parquet

License

MIT. Stockfish self-play data — no human game data.