khoilamalphaai/chess-coach-32b-v6-dpo2
Text Generation • Updated • 48
Datasets:
messages listlengths 3 3 | provenance dict |
|---|---|
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{
"role": "system",
"content": "You are a chess coach doing move review for a student at a stated rating tier.\n\nYou are given the position, the move the student made, and engine analysis: a list\nof sound candidate moves (with internal evaluations and short lines) plus how likely\na human at that tier is ... | {
"pos_id": "puz_02h4a",
"fen": "r5kr/4P3/2pB1Rpp/1pPp2n1/p2P4/2P1P3/7P/1R5K w - - 3 32",
"tier": "intermediate",
"phase": "middlegame",
"n_pieces": 20,
"source": "mined_puzzle",
"game_id": "puz_02h4a",
"engine_best": {
"uci": "b1f1",
"san": "Rbf1",
"cp": 1083,
"wdl": [
1000,
... |
[
{
"role": "system",
"content": "You are a chess coach doing move review for a student at a stated rating tier.\n\nYou are given the position, the move the student made, and engine analysis: a list\nof sound candidate moves (with internal evaluations and short lines) plus how likely\na human at that tier is ... | {
"pos_id": "cpiPC4PW_42",
"fen": "2kr3r/1pp2pp1/p1n4p/8/1P2n3/P2pbN1P/3N2PB/R2Q1K1R b - - 1 21",
"tier": "advanced",
"phase": "middlegame",
"n_pieces": 24,
"source": "reused",
"game_id": "cpiPC4PW",
"engine_best": {
"uci": "f7f5",
"san": "f5",
"cp": -336,
"wdl": [
0,
15,
... |
[
{
"role": "system",
"content": "You are a chess coach doing move review for a student at a stated rating tier.\n\nYou are given the position, the move the student made, and engine analysis: a list\nof sound candidate moves (with internal evaluations and short lines) plus how likely\na human at that tier is ... | {
"pos_id": "3QoqOY2B_42",
"fen": "5rk1/pr1q1p1p/1p4p1/3Nb3/4P3/1Q3P2/PP4PP/R3R1K1 b - - 0 21",
"tier": "advanced",
"phase": "middlegame",
"n_pieces": 21,
"source": "reused",
"game_id": "3QoqOY2B",
"engine_best": {
"uci": "g8h8",
"san": "Kh8",
"cp": -151,
"wdl": [
5,
359,
... |
[
{
"role": "system",
"content": "You are a chess coach doing move review for a student at a stated rating tier.\n\nYou are given the position, the move the student made, and engine analysis: a list\nof sound candidate moves (with internal evaluations and short lines) plus how likely\na human at that tier is ... | {
"pos_id": "puz_0fFb1",
"fen": "8/8/8/8/p1P3pk/8/1P3K2/8 b - - 0 51",
"tier": "intermediate",
"phase": "endgame",
"n_pieces": 6,
"source": "mined_puzzle",
"game_id": "puz_0fFb1",
"engine_best": {
"uci": "h4g5",
"san": "Kg5",
"cp": 0,
"wdl": [
85,
830,
85
]
},
"... |
[
{
"role": "system",
"content": "You are a chess coach doing move review for a student at a stated rating tier.\n\nYou are given the position, the move the student made, and engine analysis: a list\nof sound candidate moves (with internal evaluations and short lines) plus how likely\na human at that tier is ... | {
"pos_id": "puz_05oox",
"fen": "6k1/5pp1/1BQ4p/P2p2q1/8/3b1P2/6PP/6K1 w - - 1 34",
"tier": "intermediate",
"phase": "middlegame",
"n_pieces": 14,
"source": "mined_puzzle",
"game_id": "puz_05oox",
"engine_best": {
"uci": "c6c3",
"san": "Qc3",
"cp": 14,
"wdl": [
108,
826,
... |
[
{
"role": "system",
"content": "You are a chess coach doing move review for a student at a stated rating tier.\n\nYou are given the position, the move the student made, and engine analysis: a list\nof sound candidate moves (with internal evaluations and short lines) plus how likely\na human at that tier is ... | {
"pos_id": "5Yscxpu7_28",
"fen": "r5k1/ppp3p1/2np1rp1/b3p1N1/4P2P/2PPB3/PP3PP1/RN2K2R b KQ - 2 14",
"tier": "intermediate",
"phase": "middlegame",
"n_pieces": 26,
"source": "reused",
"game_id": "5Yscxpu7",
"engine_best": {
"uci": "b7b5",
"san": "b5",
"cp": -580,
"wdl": [
0,
... |
[
{
"role": "system",
"content": "You are a chess coach doing move review for a student at a stated rating tier.\n\nYou are given the position, the move the student made, and engine analysis: a list\nof sound candidate moves (with internal evaluations and short lines) plus how likely\na human at that tier is ... | {
"pos_id": "puz_0hZOj",
"fen": "2Q5/5pk1/8/6Kp/8/2P2qP1/8/8 w - - 10 58",
"tier": "intermediate",
"phase": "endgame",
"n_pieces": 8,
"source": "mined_puzzle",
"game_id": "puz_0hZOj",
"engine_best": {
"uci": "c8d7",
"san": "Qd7",
"cp": 0,
"wdl": [
85,
830,
85
]
},... |
End of preview. Expand in Data Studio
Chess Coach v6 (deep-verified training labels)
The current data frontier for the chess-instructor-llm coach: a foundational, data-first rebuild of the training LABELS (the move plus full provenance), deep-verified with Stockfish 17 (a two-depth root search with agreement bands), Syzygy tablebases (endgames of seven pieces or fewer), and Maia-2 human-likelihood. It feeds the downstream preference (DPO) and engine-distillation retrains.
This dataset is NOT the shipped SFT set. The shipped coach (chess-coach-32b-v4-qlora) is trained on chess-coach-move-review (default config v4), which is left untouched. v6 is the deeper-verified successor used to train the stretch adapters below.
Configs and splits
| Config | Split | File | Rows |
|---|---|---|---|
| default | train | train_v6.jsonl |
6,768 |
| default | validation | valid_v6.jsonl |
363 |
| scenarios | test | scenarios_v6.jsonl |
2,409 |
from datasets import load_dataset
ds = load_dataset("khoilamalphaai/chess-coach-v6") # train + validation (SFT rows)
bench = load_dataset("khoilamalphaai/chess-coach-v6", "scenarios") # benchmark scenarios
The validation split is a game-disjoint holdout. The benchmark scenarios keep the 120 held-out TEST board ids stable across the rebuild (360 val rows), so v6-labelled scores are directly comparable to the shipped v4 headline.
Row schema
train_v6.jsonlandvalid_v6.jsonl: each row is{"messages": [...], "provenance": {...}}.messagesis an OpenAI-style chat triple (system, user, assistant) carrying the coaching label;provenancecarries the auditable grounding for that label (pos_id,fen,tier,phase,engine_best,canonical_uci/canonical_san,canonical_pool_rank,canonical_is_engine_best,maia_policy,severity,student,review_action,discriminating, and more).scenarios_v6.jsonl: the benchmark labels, one row per position and tier, with the full deep-verifiedsound_pool,rejectedmoves,engine_best,canonicalmove,maiapolicy, and the engine settings used.
What changed versus v4 (the audit fixes, in the data)
| Fix | Result |
|---|---|
| Deep, robust sound pool (SF17 depth 14 and 20, agreement bands, WDL, Syzygy) | 28.9% of the old benchmark sound-pool moves are rejected as not-actually-sound under deep search |
| Advanced tier fixed to the verified engine-best move | old benchmark advanced not-equal-to engine_best on 43 positions; v6 has 0 |
| Complete triads by construction | every board carries all three tiers, with no collapsed levels |
| Re-derived training labels | 45.5% of comparable training labels changed (3,307 of 7,269) |
Dataset stats (v6 manifest): 2,377 unique boards, 2,123 discriminating boards, rows balanced across tiers (2,377 beginner / intermediate / advanced), sources reused 3,210 and freshly mined 3,921.
Engine configuration
stockfish 17, root-search depths [14, 20], time caps [1.0s, 6.0s], MultiPV 10,
tolerance 120 cp, Maia = maia2-rapid at 1100 / 1500 / 1900,
Syzygy = Lichess tablebase API (<= 7 pieces)
What the labels feed (stretch adapters)
| Adapter | Uses | Honest result (corrected 120 TEST) |
|---|---|---|
| chess-coach-32b-v6-dpo | canonical (chosen) vs contrast (rejected) pairs | tier-policy 0.881 grounded, gain in the intermediate tier |
| chess-coach-32b-v6-dpo2 | harder tier-targeted pairs | tier-policy 0.892 grounded, best DPO, gain in the intermediate tier |
| chess-coach-32b-v6-distill | verified engine-best targets | tier-policy 0.325 no-grounding, behavior-in-weights with an advanced-tier limit |
Honest caveats
- Tier-policy match is agreement with the project's
select_tier_moverule, a learnability metric, not certified best teaching. - The advanced label is by construction the sharp engine-best move, so advanced rows largely mirror the engine; the distinctive leveling signal is strongest at the beginner and intermediate tiers.
- Move labels are engine-verified against the deep sound pool, but the coaching prose is distilled from a teacher model. Truthfulness of that prose is enforced at serving time by a separate non-LLM verifier, not by the data.
- Grounding is required at inference for the grounded adapters; without the per-tier Maia signal the three tiers collapse to a single move.
Links and license
| Resource | Link |
|---|---|
| Shipped model (v4) | chess-coach-32b-v4-qlora |
| Shipped SFT dataset (v4) | chess-coach-move-review |
| Demo | chess-coach-studio |
| Code / GitHub repo | Alpha-AI-Engineering-Khoi/chess-instructor-llm |
Positions derive from the public CC0 Lichess Open Database; the coaching text is distilled from GPT-5.5. Released for research and education under CC-BY-NC-4.0.
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