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-Hard 30×30	87.0% exact-match	single-z, conv1d/k4, non-augmented; eval @ max_iter 35k, decay 0.996/pat 10
sudoku/	Sudoku-Extreme	94.2% exact-match	single-z, conv2d/k3, norm-placement=none; eval @ max_iter 35k, decay 0.997/pat 10
arc1/	ARC-1-concept (aug-1000)	47.5% pass@2	single-z, conv1d/k4, norm-placement=none; eval @ max_iter 1000
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).