LorMolf/SPSD-Variants-opsd
SPSD-Variants-opsd Grounded on-policy self-distillation (OPSD) teacher-context dataset over 45 board-game rule variants (5 families × 9: connect4, domineering, simplified_first_attack, simplified_othello, tic_tac_chess), derived from trained MuZero/EfficientZero checkpoints (plan-528 v2). Each row is a decision-state task (a move choice or one of six auxiliary state-QA tasks). The privileged_context is the teacher signal: grounded natural-language reasoning that discovers the… See the full description on the dataset page: https://huggingface.co/datasets/LorMolf/SPSD-Variants-opsd.
SPSD-Variants-opsd
Grounded on-policy self-distillation (OPSD) teacher-context dataset over 45 board-game rule variants (5 families × 9: connect4, domineering, simplifiedfirstattack, simplifiedothello, tictac_chess), derived from trained MuZero/EfficientZero checkpoints (plan-528 v2).
Each row is a decision-state task (a move choice or one of six auxiliary state-QA tasks). The `privileged_context` is the teacher signal: grounded natural-language reasoning that discovers the best move over the tempting alternative and verifies it against the opponent's best reply, grounded in fresh MuZero-net values that choose the wording but are never printed, and never naming the search engine. The student-facing prompt, the completion, and the target are unchanged from the underlying pools — only privileged_context carries the grounded reasoning.
Contents
Files are gzip-compressed JSON Lines (datasets loads .jsonl.gz transparently).
Per-family subsets are obtained by filtering metadata.game_name / metadata.family (the five families are connect4, domineering, simplifiedfirstattack, simplifiedothello, tictac_chess).
State-QA tasks (six): occupancy, legality, threat_count, legal_action_count, legal_action_enumeration, successor_state.
Row schema
prompt— student-facing board task (chat messages).completion—Final answer: \boxed{…}.privileged_context— grounded teacher reasoning + the boxed answer (teacher-only).target/accepted_answers— verifier + canonical answer.metadata—task,game_name,decision_state_hash, checkpoint provenance (path/alias/SHA/env-step, algorithm,num_simulations, backend), andreplay_evidence_horizon(richvs a conservative-fallback mode).
Provenance & method
- Sources: the trained
simplified5othello-variant EfficientZero checkpoints; 36 variants viarandom-prefix-full-gamecheckpoint self-play, 9 via exhaustive real-env enumeration. - Grounding: one fresh 50-simulation MCTS search per decision state; a separation gate (min root gap 0.05) refuses a distinction the root value does not support and falls back to conservative prose (no row is ever dropped).
- Move coverage: ~54% rich grounded traces, ~46% conservative fallback.
- Seed 0. Verified corpus-wide: no raw search stats or engine names leak into
privileged_context; state-QA rationales are array-free.
Caveats
- Text only (
tokenizer_hash=not_tokenized): tokenize and build the OPSD-ready rows for your trainer. - 6 connect4 variants (
exact_four,holey_four,no_center_opening,one_diagonal_disabled,orthogonal_only,outer_opening) carry a declaredlegalityshortfall (positive-skewed legality QA — illegal-handle states are rare there); the pooled answer-class balance stays ≤ 0.65. - Global per-task counts are balanced per family, not equal across the corpus (reported, not enforced).
Private research dataset.
