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Datasets:
Chess-Nut-Engine
/
chess-sft-corpus-4x

Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
1M - 10M
Tags:
chess
sft
instruction-tuning
reasoning
chess960
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
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metadata 
license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - chess
  - sft
  - instruction-tuning
  - reasoning
  - chess960
pretty_name: Chess SFT Training Data
configs:
  - config_name: default
    data_files:
      - split: train
        path: tier*/*.jsonl
  - config_name: tier0_tier0
    data_files:
      - split: train
        path: tier0/*.jsonl
  - config_name: tier1_perception
    data_files:
      - split: train
        path: tier1/*.jsonl
  - config_name: tier2_rules
    data_files:
      - split: train
        path: tier2/*.jsonl
  - config_name: tier3_tactics
    data_files:
      - split: train
        path: tier3/*.jsonl
  - config_name: tier4_evaluation
    data_files:
      - split: train
        path: tier4/*.jsonl
  - config_name: tier5_openings
    data_files:
      - split: train
        path: tier5/*.jsonl
  - config_name: tier6_endgames
    data_files:
      - split: train
        path: tier6/*.jsonl
  - config_name: tier7_planning
    data_files:
      - split: train
        path: tier7/*.jsonl

Chess SFT Training Data

A supervised fine-tuning dataset for teaching language models to reason about chess. It covers board perception, legal move generation, tactics, evaluation, openings, endgames, and planning.

Total examples 15,100,000
Total size 18902.7 MB
Format JSONL chat rows with messages
Eval companion Chess-Nut-Engine/chess-sft-eval

Tier Overview

Tier Category Tasks Examples Size
0 Unknown 1 300,000 556.2 MB
1 Perception 19 6,240,000 8029.8 MB
2 Rules 14 3,280,000 4298.8 MB
3 Tactics 9 1,800,000 1759.3 MB
4 Evaluation 3 600,000 447.9 MB
5 Openings 3 120,000 101.1 MB
6 Endgames 4 560,000 386.8 MB
7 Planning 14 2,200,000 3322.7 MB

Loading 

from datasets import load_dataset

ds = load_dataset("Chess-Nut-Engine/chess-sft-corpus-4x", streaming=True)
ds_tier1 = load_dataset("Chess-Nut-Engine/chess-sft-corpus-4x", "tier1_perception")

Row Schema

Every row is one JSON object:

Field Type Meaning
task str Curriculum task id (e.g. 2.1_legal_move_gen) — filter/join key
tier int Curriculum tier 1-7
fen str Anchor position (FEN)
is_chess960 bool Chess960 row
messages list Chat turns (system/user/assistant); 7.12_game_episode rows are multi-turn
metadata object Task-specific provenance (expected answers, source ids, dedup identity)

Filtering by task across configs:

legal = ds["train"].filter(lambda row: row["task"] == "2.1_legal_move_gen")

Data Sources

Rows are generated from Lichess games, Lichess puzzles, Lichess openings, Lichess position evaluations, MATE rows, Syzygy tablebases, Polyglot opening books, and generated Chess960 positions. Eval and benchmark FENs are excluded from training with a blocklist (game-scoped: sibling positions of eval games are blocked too).

Provenance

  • SDPO seed pack (2026-07-09): five new tier-7 tasks seeding RL-amplifiable reasoning behaviors, all engine/board-verified. 7.14_refute_and_switch (120k): tempting candidate -> its own engine-PV refutation with verified consequence -> 'Backtrack:' -> best. 7.15_composed_audit (120k): Check/Material/Captures/Hanging skill pipeline inline, then rated candidates. 7.16_prose_analysis (120k): grounded natural-language analysis in three styles (problem-focused / intuition-then-verify / comparison), phrase-pool diversity, zero unverified claims. 7.17_puzzle_episode (160k): full forced puzzle solutions as multi-turn episodes (opponent replies injected as user turns). 7.18_line_tracking_episode (120k): state the engine PV as a plan, execute it across turns ('On plan: was expected.'). Intended as an SFT top-up before GRPO/SDPO.
  • Also refreshed via identity-dedup resume: 7.2_puzzle_solving, 7.11_history_best_move.

Detailed File Listing

Tier 0 - Unknown

File Task Examples Size
tier0/0.1_general_instruct.jsonl 300,000 556.2 MB

Tier 1 - Perception

File Task Examples Size
tier1/1.10_fen_row_application.jsonl 400,000 781.0 MB
tier1/1.11_square_coordinates.jsonl 400,000 411.6 MB
tier1/1.12_fen_rank_expansion.jsonl 400,000 397.4 MB
tier1/1.13_fen_rank_cell_edit.jsonl 400,000 373.1 MB
tier1/1.14_fen_board_edit.jsonl 400,000 523.7 MB
tier1/1.15_material_inventory.jsonl 200,000 237.1 MB
tier1/1.16_material_piece_counts.jsonl 200,000 216.0 MB
tier1/1.17_material_value_totals.jsonl 200,000 224.7 MB
tier1/1.18_material_balance_trace.jsonl 200,000 319.0 MB
tier1/1.19_multi_move_state_tracking.jsonl Apply two to three moves and report the resulting FEN 160,000 390.7 MB
tier1/1.1_fen_to_board.jsonl Render a FEN string as a human-readable board diagram 320,000 242.9 MB
tier1/1.2_board_to_fen.jsonl Convert a board diagram back to FEN notation 320,000 314.3 MB
tier1/1.3_piece_identification.jsonl Identify which piece occupies a given square 400,000 359.3 MB
tier1/1.4_piece_counting.jsonl Count pieces of a specific type/color on the board 320,000 516.2 MB
tier1/1.5_state_tracking.jsonl Apply moves and report the resulting position 400,000 656.0 MB
tier1/1.6_square_lookup.jsonl Read a single square's contents from FEN 400,000 327.6 MB
tier1/1.7_rank_lookup.jsonl Read one compressed rank row from FEN 320,000 257.8 MB
tier1/1.8_move_square_edits.jsonl Trace square lookups and rank edits for one move 400,000 659.2 MB
tier1/1.9_fen_assembly.jsonl Apply one move and assemble the resulting full FEN 400,000 822.1 MB

Tier 2 - Rules

File Task Examples Size
tier2/2.0_side_piece_inventory.jsonl List side-to-move pieces and squares before move generation 240,000 374.4 MB
tier2/2.10_ray_walk.jsonl Walk each slider ray square by square to derive its moves 200,000 305.5 MB
tier2/2.11_legal_filter_trace.jsonl Filter every piece's pseudo-legal moves into rejected and legal moves 160,000 445.3 MB
tier2/2.12_illegal_move_correction.jsonl Recover from a rejected illegal move by choosing a legal one 160,000 155.4 MB
tier2/2.13_check_evasion.jsonl Enumerate every legal escape from check: king moves, checker captures, blocks 160,000 196.7 MB
tier2/2.1_legal_move_gen.jsonl List all legal moves for the side to move 320,000 493.4 MB
tier2/2.2_piece_specific_moves.jsonl List legal moves for a specific piece 320,000 258.0 MB
tier2/2.3_move_legality_check.jsonl Determine whether a move is legal 320,000 271.2 MB
tier2/2.4_check_detection.jsonl Detect check, checkmate, or stalemate 240,000 183.5 MB
tier2/2.5_special_rules.jsonl Handle castling, en passant, promotion, and 50-move rule 200,000 171.6 MB
tier2/2.6_piece_pseudo_legal_moves.jsonl 240,000 234.9 MB
tier2/2.7_piece_legal_filter.jsonl 240,000 325.1 MB
tier2/2.8_king_safety_filter.jsonl 240,000 262.5 MB
tier2/2.9_legal_moves_by_piece.jsonl 240,000 621.5 MB

Tier 3 - Tactics

File Task Examples Size
tier3/3.1_available_captures.jsonl Find available capture moves 240,000 159.4 MB
tier3/3.2_threats.jsonl Identify pieces that are threatening enemy pieces 200,000 133.5 MB
tier3/3.3_attacked_defended.jsonl Count attackers and defenders of a queried square 240,000 290.0 MB
tier3/3.4_tactical_patterns.jsonl Recognize tactical motifs 200,000 161.9 MB
tier3/3.5_hanging_pieces.jsonl Find undefended pieces that can be captured 160,000 114.2 MB
tier3/3.6_hanging_piece_status.jsonl Classify whether one piece is attacked, defended, and hanging 200,000 205.2 MB
tier3/3.7_hanging_piece_filter.jsonl Audit attacked pieces and filter defended decoys from hanging pieces 200,000 247.2 MB
tier3/3.8_hanging_piece_claim_verification.jsonl Verify and correct hanging-piece claims 200,000 268.8 MB
tier3/3.9_static_exchange_evaluation.jsonl Work out capture recapture sequence and net material 160,000 179.1 MB

Tier 4 - Evaluation

File Task Examples Size
tier4/4.1_material_balance.jsonl Count material and compute the balance 200,000 155.7 MB
tier4/4.2_position_evaluation.jsonl Evaluate a position from engine-calibrated labels 240,000 180.7 MB
tier4/4.3_pawn_structure.jsonl Analyze pawn structure 160,000 111.5 MB

Tier 5 - Openings

File Task Examples Size
tier5/5.1_opening_identification.jsonl Name the opening from a position or line 40,000 32.2 MB
tier5/5.2_opening_continuation.jsonl Suggest the next book move 40,000 33.8 MB
tier5/5.3_opening_principles.jsonl Explain opening principles 40,000 35.1 MB

Tier 6 - Endgames

File Task Examples Size
tier6/6.1_endgame_classification.jsonl Classify the endgame material 120,000 80.6 MB
tier6/6.2_endgame_wdl.jsonl Predict tablebase win/draw/loss 160,000 107.0 MB
tier6/6.3_endgame_best_move.jsonl Find a tablebase-backed endgame move 160,000 107.7 MB
tier6/6.4_endgame_principles.jsonl Explain endgame principles 120,000 91.5 MB

Tier 7 - Planning

File Task Examples Size
tier7/7.10_best_line_trace.jsonl Emit a fixed-grammar Stockfish best-line trace 120,000 171.3 MB
tier7/7.11_history_best_move.jsonl Imitate the next move from a game prefix and current FEN 160,000 209.0 MB
tier7/7.12_game_episode.jsonl Play a multi-turn game window move by move from a FEN anchor 160,000 271.2 MB
tier7/7.13_candidate_compare_trace.jsonl Answer a best-move prompt by rating MultiPV candidates inside think before committing 160,000 345.3 MB
tier7/7.14_refute_and_switch.jsonl Consider a tempting move, refute it with the engine line, backtrack to the best move 120,000 219.6 MB
tier7/7.15_composed_audit.jsonl Audit check/material/captures/hanging inline, then rate candidates and choose 120,000 239.1 MB
tier7/7.16_prose_analysis.jsonl Analyze a position in grounded prose (problem/intuition/comparison style), then give the move 120,000 275.1 MB
tier7/7.17_puzzle_episode.jsonl Play out a forced puzzle solution turn by turn against injected opponent replies 160,000 184.3 MB
tier7/7.18_line_tracking_episode.jsonl State the engine PV as a plan, then execute it across turns tracking the expected replies 120,000 193.4 MB
tier7/7.1_best_move_selection.jsonl Select the best move from engine-evaluated positions 320,000 315.9 MB
tier7/7.2_puzzle_solving.jsonl Solve a tactical puzzle 200,000 208.2 MB
tier7/7.3_move_consequence.jsonl Predict the consequence of a candidate move 160,000 169.0 MB
tier7/7.8_candidate_ratings.jsonl Rate five Stockfish MultiPV candidate moves with fixed grammar 160,000 280.2 MB
tier7/7.9_step_verification.jsonl Audit a numbered chess trace and identify one broken step 120,000 241.1 MB