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BIGR backbones, intermediate checkpoints and evaluation splits

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  1. README.md +46 -0
  2. maze_hard/checkpoints/last.ckpt +3 -0
  3. maze_hard/dataset/maze-30x30-hard-1k/impossible/all__group_indices.npy +3 -0
  4. maze_hard/dataset/maze-30x30-hard-1k/impossible/all__inputs.npy +3 -0
  5. maze_hard/dataset/maze-30x30-hard-1k/impossible/all__labels.npy +3 -0
  6. maze_hard/dataset/maze-30x30-hard-1k/impossible/all__puzzle_identifiers.npy +3 -0
  7. maze_hard/dataset/maze-30x30-hard-1k/impossible/all__puzzle_indices.npy +3 -0
  8. maze_hard/dataset/maze-30x30-hard-1k/impossible/dataset.json +10 -0
  9. maze_hard/dataset/maze-30x30-hard-1k/test/all__group_indices.npy +3 -0
  10. maze_hard/dataset/maze-30x30-hard-1k/test/all__inputs.npy +3 -0
  11. maze_hard/dataset/maze-30x30-hard-1k/test/all__labels.npy +3 -0
  12. maze_hard/dataset/maze-30x30-hard-1k/test/all__puzzle_identifiers.npy +3 -0
  13. maze_hard/dataset/maze-30x30-hard-1k/test/all__puzzle_indices.npy +3 -0
  14. maze_hard/dataset/maze-30x30-hard-1k/test/dataset.json +10 -0
  15. ppbench/checkpoints/last.ckpt +3 -0
  16. sudoku_extreme/checkpoints/epoch_12499.ckpt +3 -0
  17. sudoku_extreme/checkpoints/epoch_14999.ckpt +3 -0
  18. sudoku_extreme/checkpoints/epoch_17499.ckpt +3 -0
  19. sudoku_extreme/checkpoints/epoch_19999.ckpt +3 -0
  20. sudoku_extreme/checkpoints/epoch_22499.ckpt +3 -0
  21. sudoku_extreme/checkpoints/epoch_2499.ckpt +3 -0
  22. sudoku_extreme/checkpoints/epoch_24999.ckpt +3 -0
  23. sudoku_extreme/checkpoints/epoch_27499.ckpt +3 -0
  24. sudoku_extreme/checkpoints/epoch_29999.ckpt +3 -0
  25. sudoku_extreme/checkpoints/epoch_32499.ckpt +3 -0
  26. sudoku_extreme/checkpoints/epoch_34999.ckpt +3 -0
  27. sudoku_extreme/checkpoints/epoch_37499.ckpt +3 -0
  28. sudoku_extreme/checkpoints/epoch_39999.ckpt +3 -0
  29. sudoku_extreme/checkpoints/epoch_42499.ckpt +3 -0
  30. sudoku_extreme/checkpoints/epoch_44999.ckpt +3 -0
  31. sudoku_extreme/checkpoints/epoch_47499.ckpt +3 -0
  32. sudoku_extreme/checkpoints/epoch_4999.ckpt +3 -0
  33. sudoku_extreme/checkpoints/epoch_49999.ckpt +3 -0
  34. sudoku_extreme/checkpoints/epoch_7499.ckpt +3 -0
  35. sudoku_extreme/checkpoints/epoch_9999.ckpt +3 -0
  36. sudoku_extreme/checkpoints/last.ckpt +3 -0
  37. sudoku_extreme/dataset/sudoku_extreme/impossible/all__group_indices.npy +3 -0
  38. sudoku_extreme/dataset/sudoku_extreme/impossible/all__inputs.npy +3 -0
  39. sudoku_extreme/dataset/sudoku_extreme/impossible/all__labels.npy +3 -0
  40. sudoku_extreme/dataset/sudoku_extreme/impossible/all__puzzle_identifiers.npy +3 -0
  41. sudoku_extreme/dataset/sudoku_extreme/impossible/all__puzzle_indices.npy +3 -0
  42. sudoku_extreme/dataset/sudoku_extreme/impossible/dataset.json +12 -0
  43. sudoku_extreme/dataset/sudoku_extreme/test/all__group_indices.npy +3 -0
  44. sudoku_extreme/dataset/sudoku_extreme/test/all__inputs.npy +3 -0
  45. sudoku_extreme/dataset/sudoku_extreme/test/all__labels.npy +3 -0
  46. sudoku_extreme/dataset/sudoku_extreme/test/all__puzzle_identifiers.npy +3 -0
  47. sudoku_extreme/dataset/sudoku_extreme/test/all__puzzle_indices.npy +3 -0
  48. sudoku_extreme/dataset/sudoku_extreme/test/dataset.json +12 -0
README.md ADDED
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+ ---
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+ license: mit
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+ library_name: pytorch
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+ tags:
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+ - recursive-reasoning
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+ - particle-filter
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+ - sudoku
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+ - maze
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+ - test-time-compute
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+ ---
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+
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+ # BIGR: frozen TRM backbones and evaluation splits
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+
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+ Assets for the NeurIPS 2026 paper *Boosting Inference with Guided Reasoning: Stochastic
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+ Exploration for Recursive Models* (Corbett, Tzatzopoulou, Sood, Ramesh, Dodwell).
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+ Code and reproduction recipes: https://github.com/digiLab-ai/bigr. Paper:
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+ https://openreview.net/forum?id=nZD0FHMUmR.
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+
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+ This repository mirrors the `data/` tree that the code expects, so
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+ `python scripts/download_checkpoints.py` places every file where the experiment scripts look:
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+
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+ ```
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+ sudoku_extreme/checkpoints/last.ckpt Sudoku-Extreme backbone (TRM, 7M parameters, 74 MB)
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+ sudoku_extreme/checkpoints/epoch_*.ckpt the 20 intermediate checkpoints of the same run (Figure 8)
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+ sudoku_extreme/dataset/sudoku_extreme/test/ the 10,020-puzzle Test split evaluated in the paper
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+ sudoku_extreme/dataset/sudoku_extreme/impossible/ the 1,427-puzzle deterministic-failure (DF) split
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+ maze_hard/checkpoints/last.ckpt Maze-Hard backbone (104 MB)
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+ maze_hard/dataset/maze-30x30-hard-1k/test/ the 1,000-maze Test split
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+ maze_hard/dataset/maze-30x30-hard-1k/impossible/ the 134-maze DF split
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+ ppbench/checkpoints/last.ckpt PPBench backbone (106 MB)
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+ ```
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+
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+ The checkpoints are PyTorch Lightning checkpoints of the vendored nano-trm `TRMModule`
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+ (EMA weights included); the code loads them with `bigr.load_trm_backbone_checkpoint`.
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+ Nothing is trained for inference: the Q-head that BIGR uses as its guide is part of every
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+ checkpoint. Training configs and commands are in the code repository.
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+
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+ The evaluation splits are in the TRM on-disk format (`all__*.npy` + `dataset.json`). They
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+ are derived subsets of the Hierarchical Reasoning Model releases
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+ `sapientinc/sudoku-extreme` and `sapientinc/maze-30x30-hard-1k`, redistributed here for
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+ reproducibility only; the same splits can be rebuilt from those releases with
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+ `python scripts/data/build_eval_cohorts.py` in the code repository. The DF split of each
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+ benchmark is the set of Test puzzles the frozen backbone fails to solve deterministically.
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+
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+ The checkpoints are released under the MIT licence; see `NOTICES.md` in the code
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+ repository for the licences of the datasets and of the backbone code they were trained with.
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