---
license: mit
---

# FPRM — Fixed-Point Tiny Recursive Models

Checkpoints for **FPRM / FPTRM** (Fixed-Point Tiny Recursive Model), a weight-tied
iterative reasoner trained with a fixed-point solver and test-time compute scaling.

Each subfolder is one self-contained run (checkpoints + exact config + model source +
reproduction scripts). See each folder's `README.md` for the full recipe and results.

| folder | task | best test metric | notes |
|---|---|---|---|
| [`maze/`](maze) | Maze-Hard 30×30 | **87.0%** exact-match | single-z, conv1d/k4, non-augmented; eval @ max_iter 35k, decay 0.996/pat 10 |
| [`sudoku/`](sudoku) | Sudoku-Extreme | **94.2%** exact-match | single-z, conv2d/k3, norm-placement=none; eval @ max_iter 35k, decay 0.997/pat 10 |
| [`arc1/`](arc1) | ARC-1-concept (aug-1000) | **47.5%** pass@2 | single-z, conv1d/k4, norm-placement=none; eval @ max_iter 1000 |
| [`arc2/`](arc2) | ARC-2-concept (aug-1000) | **6.2%** pass@2 | single-z, conv1d/k4, norm-placement=none; eval @ max_iter 1000 |

Checkpoints are saved every 5000 epochs as `step_<N>` (EMA-averaged eval weights, the ones
to load) and `step_<N>_train_state.pt` (full training state for resuming). The `arc1/` and
`arc2/` folders ship eval checkpoints only (no `train_state`).