---
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.