README.md

metadata

license: other
task_categories:
  - text-generation
tags:
  - board-games
  - reasoning
  - self-distillation
  - opsd
  - mcts
pretty_name: SPSD-Variants OPSD (grounded MCTS teacher context, 45 variants)
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.jsonl.gz
      - split: heldout_move
        path: frozen_probes/move.jsonl.gz
      - split: heldout_state_qa
        path: frozen_probes/state_qa.jsonl.gz

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 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).

file	rows	purpose
train.jsonl.gz	213,406	training split (170,725 move + 42,681 state-QA)
frozen_probes/move.jsonl.gz	6,840	held-out move probe (disjoint from train)
frozen_probes/state_qa.jsonl.gz	40,841	held-out six-task state-QA probe
manifest.json	—	full provenance, per-variant counts, balance, census fingerprint

Per-family subsets are obtained by filtering metadata.game_name / metadata.family (the five families are connect4, domineering, simplified_first_attack, simplified_othello, tic_tac_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), and replay_evidence_horizon (rich vs a conservative-fallback mode).

Provenance & method

• Sources: the trained simplified5 othello-variant EfficientZero checkpoints; 36 variants via random-prefix-full-game checkpoint 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 declared legality shortfall (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.