| --- |
| license: cc-by-4.0 |
| library_name: pytorch |
| tags: |
| - recursive-reasoning |
| - particle-filter |
| - sudoku |
| - maze |
| - test-time-compute |
| --- |
| |
| # BIGR: frozen TRM backbones |
|
|
| **Tiny recursive models, think BIGR.** |
|
|
| Assets for the NeurIPS 2026 paper *Boosting Inference with Guided Reasoning: Stochastic |
| Exploration for Recursive Models* (Corbett, Tzatzopoulou, Sood, Ramesh, Dodwell). |
| Code and reproduction recipes: https://github.com/digiLab-ai/bigr. Paper: |
| https://openreview.net/forum?id=nZD0FHMUmR. |
|
|
| This repository mirrors the `data/` tree that the code expects, so |
| `python scripts/download_checkpoints.py` places every file where the experiment scripts look: |
|
|
| ``` |
| sudoku_extreme/checkpoints/last.ckpt Sudoku-Extreme backbone (TRM, 7M parameters, 74 MB) |
| sudoku_extreme/checkpoints/epoch_*.ckpt the 20 intermediate checkpoints of the same run (Figure 8) |
| maze_hard/checkpoints/last.ckpt Maze-Hard backbone (104 MB) |
| ppbench/checkpoints/last.ckpt PPBench backbone (106 MB) |
| ``` |
|
|
| The checkpoints are PyTorch Lightning checkpoints of the vendored nano-trm `TRMModule` |
| (EMA weights included); the code loads them with `bigr.load_trm_backbone_checkpoint`. |
| Nothing is trained for inference: the Q-head that BIGR uses as its guide is part of every |
| checkpoint. Training configs and commands are in the code repository. |
|
|
| The evaluation splits the paper scores (Sudoku-Extreme Test 10,020 and DF 1,427; Maze-Hard |
| Test 1,000 and DF 134) are not redistributed: `python scripts/data/build_eval_cohorts.py` in |
| the code repository rebuilds them from the Hierarchical Reasoning Model releases |
| `sapientinc/sudoku-extreme` and `sapientinc/maze-30x30-hard-1k`. |
|
|
| The checkpoints are released under CC BY 4.0: credit digiLab Solutions Ltd and cite the |
| paper when you use them. `NOTICES.md` in the code repository lists the licences of the |
| datasets and of the backbone code they were trained with. |
|
|
